Intel Launches First AI Associate Degree Program

Intel announced June 29 that it is working with Maricopa County Community College District (MCCCD) to start the first AI associate degree program in the US. The Arizona Commerce Authority will provide a grant of $100,000 to support the associate degree program. It will allow tens of thousands of students to get jobs in high-tech, automotive, healthcare, industrial and airspace fields.

Intel stated in a press release that it believes that artificial intelligence technology should be shaped by different people with different experiences and backgrounds. Community colleges offer the ability to diversify and expand AI as they attract a diverse body of students with different expertise and backgrounds. Intel noted that it is committed to working with colleges to expand access to technology skills that are needed for current and future employment.

MCCCD Is The Largest Community College District in the US

MCCCD is based in Tempe, Arizona, and is the biggest community college district in the United States with an enrollment of more than 100,000 students on 10 campuses, as well as 10,000 faculty and staff members.

The associate’s degree program for AI features courses that have been developed by the faculty at MCCCD and Intel leaders, based on Intel tools and software, including the Intel Distribution of OpenVINO Toolkit and Intel Python.

Students To Learn Vital AI Skills

Intel also plans to contribute technical training and advice, faculty training, summer internships, and mentors from Intel for students and faculty. Students will learn vital skills such as:

  • Data collection
  • AI model training
  • Coding
  • Exploration of AI technology’s impact on society

The associate’s degree program includes a social impact AI project that is made with guidance from teachers and Intel mentors. When students finish the program, MCCCD will provide an associate’s degree in AI that can be transferred to a four-year university. Those courses should fully transfer, and then students can earn a four-year degree in a related computer or science discipline.

Demand for AI-Trained Professionals Will Soar in Near Future

Artificial intelligence technology is growing quickly with new tools, applications, and tools that require workers to learn new skills if they want to stay competitive. Recent studies show that the demand for AI skills will grow exponentially in the next decade. A 2020 report by Linkedin found that AI skills are one of the most in-demand hard skills in the country. (Linkedin.com). Further, research by the MCCCD Workforce and Economic Development Office reports there will be a 22.4% increase for AI professionals by 2029.

As of mid-2020, this seems like a good time for many Americans to learn new, in-demand skills. As of June 2020, at least 43 million Americans applied for unemployment benefits. Also, a McKinsey study released recently stated that more than 57 million jobs could be subject to layoffs, furloughs, or being rendered unnecessary in the near future. (McKinsey.com). It is vital for colleges and corporations to work together to prepare for workforce demands in the near future.

First Phase of the Associate’s Degree Program Begins in Fall 2020

The AI associate’s degree program’s initial phase will be an online pilot at Estrella Mountain Community College and Chandler-Gilbert Community College in fall 2020. As concerns about COVID-19 decrease, it is expected that classes will start on both campuses later in the year or in early 2021.

This program expands the Intel AI for Youth program, which offers an artificial intelligence curriculum to more than 100,000 vocational and high school students in nine countries and will continue to grow around the world. It is hoped that this innovative program will provide hundreds of thousands of young people with essential AI skills so that they can have productive technology careers in the coming decades.

Intel Working On Other Programs to Train New AI Developers

Also, Intel recently worked with Udacity to create the Intel Edge AI for IoT Developers in Nanodegree Program, which intends to train one million AI developers. Intel has also committed to expanding digital resources to reach at least 30 million people in 30,000 colleges and universities in at least 30 countries.

These actions build on Intel’s goals that were recently announced in its 2030 Goals and Global Impact Challenges that reinforce the company’s commitment to make AI technology more inclusive and to expand digital readiness. (Intel.com). In that statement, Intell announced it would work with other companies to increase the speed of inclusive business practices across industries. It also would create and implement a Global Inclusion Index open standard.

Using common metrics, it is going to allow the industry to track progress in getting more women and minorities in technical and senior positions, accessible technology availability, and equal pay. Intel has already been working with Lenovo to convene tech executives and HR professionals to drive the transformation of the industry to be more inclusive.

Artificial Intelligence Predicts Severe Disease in Coronavirus Patients

Using AI, researchers at New York University have predicted which patients that were diagnosed with COVID-19 would eventually develop a serious respiratory disease, according to a clinical study released in Computers, Materials & Continua. (Healthitanalytics.com)

The spread of the coronavirus around the globe means there is a vital need to pinpoint which cases will make people seriously ill, according to the research team. (Techscience.com). Approximately 80% of the virus cases seem to be mild, but those who get the worst symptoms, usually need oxygen and ventilation for days.

AI May Be Able To Predict Who Gets ARDS

Acute respiratory distress syndrome or ARDS is a fluid buildup in the lungs that can be lethal in the elderly. It is a common feature in virus patients who decline after they are diagnosed. The research team at NYU wanted to see if artificial intelligence could be used to predict you who would get ARDS after being infected with COVID-19.

The NYU researchers gathered demographic, laboratory, and radiological findings from 55 patients who tested positive for the coronavirus at two hospitals in China. Their average age was 43. The research team then used the information to train artificial information models to get smarter as they collected more information.

The goal of the study was to design and deploy an AI tool using predictive analytics that could be used to support healthcare decision making. If future COVID-19 severity can be predicted, doctors may be better able to assess which patients are sick enough who need beds and which ones can be sent home safely.

Traditional Illness Characteristics Not Useful to Predict Critical COVID-19 Patients

One of the most vexing problems with the coronavirus is that common illness characteristics of COVID-19, such as specific patterns in lung images, fever, and a strong immune response were unhelpful in deciding which patients with mild symptoms would become critically ill.

Age and gender also were not useful to predict who would get really sick; however, previous studies have shown that males over 60 have a higher risk of severe respiratory illness.

AI Tool Found 3 Critical Parameters That Indicate Higher Risk of ARDS

The team’s AI tool noted changes in three important features – the level of liver enzyme called alanine aminotransferase, reported myalgia, and levels of hemoglobin were predictive of severe respiratory disease. With these critical factors in mind, the team was able to predict who was at risk of ARDS with 80% accuracy.

The clinical researchers also noted that ALT levels increase quickly as diseases such as hepatitis ravage the liver. ALT levels were only a little higher in COVID-19 patients. But they still were important in predicting the severity of the disease.

Myalgia is a deep ache of the muscles and was more common in virus patients. Past clinical research also suggests myalgia is connected to higher inflammation levels in the body.

Elevated levels of hemoglobin, a protein that contains iron that allows blood cells to carry oxygen, were also strongly connected to respiratory distress. The team noted that this may be explained by other factors, such as the patient not reporting tobacco use; smoking has been connected to higher hemoglobin levels.

The study had some limitations, such as the small dataset and the limited severity of the disease in the population that was studied. More refining of the model using more information from other settings will help to enhance its power to predict events.

Model Shows How AI Can Support Physicians

Scientists on the project noted that while there needs to be more work to validate the model, it does hold promise as another helpful tool to predict who will be most vulnerable to the virus. But the AI tool only can be used to support doctors’ clinical experience in the treatment of viral infections.

The study shows how AI and predictive analytics can be used to support doctors and other clinicians, especially during a global health crisis. Researchers say predictive analytics can play a critical role in enhancing clinical skills to distinguish being people who are sick and not sick.

They stress that just as predictive text is intended to augment and not replace writers, the goal with the AI tool is not to replace clinical reasoning. Instead, the intent is to devise models that can provide helpful insight to doctors. Clinical acumen is based on personal and collective professional learning. Machine learning can offer even more insight to aid in positive patient outcomes.

Summary

The introduction of AI in healthcare is opening up new possibilities to track and monitor the COVID-19 pandemic, which may help improve clinical care responses and outcomes.

The use of this AI tool will encourage doctors to pay attention to the data during clinical practice and to watch patients more to see if they, for example, complain of severe myalgia. Being able to share data with the field in real-time is extremely useful in a pandemic such as we face now.

America 2.0 Will Rely Heavily on Artificial Intelligence, Say Mark Cuban and Steve Bannon

Business in America is going to look very different after the coronavirus has faded, according to billionaire Mark Cuban in an interview this week on the War Room podcast created by Steve Bannon, Raheem Kassam, and Jason Miller. (Mediate.com)

Cuban, the Shark Tank star and Dallas Mavericks owner, said that he believes America 1.0 is gone. The country is going through a reset right now, and America 2.0 is going to emerge soon. That is where the entrepreneurial spirit needs to come into play.

Cuban believes big corporations are not going to hire the same way, and they are not going to keep employees the same way. Small companies will need to adjust considerably. There is going to be more online purchasing, he said, and overall America 2.0 will look different than America 1.0.

How Will Industries Evolve in America After the Coronavirus?

Bannon, a former White House advisor to President Trump asked Cuban how companies could evolve in America after the virus has passed. Bannon said that one’s brand, as Cuban tells it, will be associated with how well you comported and handled yourself with your workers, and this will last for years.

There will be companies that will be constructed during this and after this period that will last for years. Bannon then asked Cuban where he thinks these companies will be built.

Cuban believes artificial intelligence, robotics, and personalized medicine will be incredibly valuable. The Mavericks owner said he strongly believes in American exceptionalism. He wants this country to dominate every aspect of business life around the world.

He wants to treat the American people like family, but be a solid global citizen and ‘kick everyone’s ass.’ but to do that and bring back the millions of lost jobs during the virus, it will be necessary to have AI and other high-advanced technologies working for us.

Other Countries Lead Us in Technology, Cuban Argues

He noted that America is not the best in robotics today. Japan, China, and German are better than we are. For the US to get manufacturing back from East Asia, we have to get better at developing and using robotics.

Bannon briefly ran Trump’s 2016 presidential campaign and worked as a White House counselor for six months. He said Cuban is an entrepreneurial populist, although a ‘big government’ one.

Bannon said that Mark being an entrepreneurial populist can help the country to gear up its technology especially in AI and get America to the America 2.0 that he is discussing.

But Who Is Really Winning the AI Battle Between America and China?

Cuban alludes to China’s superiority over the US in artificial intelligence, but some say it is not as simple as that. (EETimes.com)

In the United States, China is believed to be building a significant technological lead in AI. But that belief is challenged by a recently published study on the AI chips China uses, which was written by economist Dieter Ernst. The 70-page report – titled ‘Competing in Artificial Intelligence Chips: China’s Challenge Amid Technology War – provides several answers about where China is in the AI battle with the US.

In the report, Ernst states that fears about China credibility threatening America’s leadership are not truly grounded in reality. Some of the key findings in the report regarding China’s technological capabilities:

  • China’s artificial intelligence industry is young and fractured.
  • China’s AI activities are mostly driven by the growth of AI apps.
  • Major players in the AI ecosystem in China are more interested in wheeling and dealing and are obsessed with AI chip unicorns.
  • China is late to the game with research and development in AI. The US began 60 years ago and was focused at the start on fundamental breakthrough research. On the other hand, China’s AI work did not start until the 1980s.
  • After 2000, funding and policies given by the Ministry of Science and Technology and local governments were successful, which resulted in the larger role of Chinese AI researchers in top AI conferences and journals.

Also, a common narrative in the US is that China’s top-down monolithic innovation policy has led to its economic success. The report argues this is a myth.

The complex nature between the state, the party, state-owned companies, private companies, financial institutions, and others are not helping China to accelerate their artificial intelligence development. Rather, it is being held back by the fragmented Chinese innovation system.

A key to China’s success in AI is its huge population of low-cost college graduates who will work hard, long hours to categorize massive troves of data that are needed to train AI algorithms. China may use this large data treasure trove to get ahead in the market for cheaper AI applications.

One of the most important messages that Ernst reports is that his research finds that US technology restrictions force China to bolster basic and applied AI research to catch up on core, foundational technologies.

AI Experts Rake in $1 Million Per Year. Here’s How

Artificial intelligence (AI) is part of almost every facet of human life, and there are examples we can see every day.

For example, AI has recently been used to improve the accuracy of eye tests. For more than 200 years, the Snellen chart and others have been used to test our eyesight acuity, but that may start to change with COVID-19 forcing more people to check their vision at home.

Researchers at Stanford University have come up with what they claim is a much more accurate eye test that uses AI to remove the possibility of human error and enhances accuracy.

In preliminary tests, the research team found a 74% reduction in error rate compared to the traditional Snellen test and a 67% reduction in errors with the best digital exam. (RTinsights.com). It is believed that having much more accurate eye tests using AI will provide people with better eyesight and also will reduce the chances of missing eye diseases during traditional eye examinations.

Another example of the importance of AI today is the House of Representatives believes military AI programs are so vital they should be overseen by the Dep. Secretary of Defense. Also, Carnegie Mellon has invested $5 million to learn how AI can help with the COVID-19 outbreak. And all of this AI news happened in just a day this month.

Can You See Why AI and Machine Learning Experts Are Making $1 Million Per Year?

If you are interested in learning about AI and making big bucks, now is your opportunity! There is an excellent AI training bundle called The Ultimate Artificial Intelligence Scientist Certification Bundle for only $34.99.

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Details of the certification bundle courses:

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    • Access nearly 300 lectures and 40 hours of content whenever you like
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10 Companies Hiring the Most AI Experts in the U.S.

While 2019 saw a dip in venture capital funding, there was one major area where that trend didn’t hold true — artificial intelligence. AI startups combined to raise nearly $19 billion in funding during 2019 alone, setting a single-year startup funding record in the process.

The startup space may be ideal for some individuals with experience in AI developing, programming and other areas, these professionals don’t have to limit their job search to fledgling companies. That’s because many of the organizations that are most desperately seeking to hire AI experts are among some of the biggest household names in the United States.

In fact, according to an analysis of job openings throughout the U.S. that were posted on Indeed.com, a leading online job board, more than 3,400 jobs across the U.S. called for experts in artificial intelligence, including engineers, programmers, analysts, instructors, researchers and more.

Before we dive into the 10 companies hiring the most AI experts for 2020, let’s talk a bit about our methodology: We believe our analysis is conservative and that more AI jobs are out there than what we’ve listed here. But to ensure that our analysis included only jobs that require direct experience in the technology, programming and science that goes into AI and machine learning, we limited our search only to jobs whose titles included one or more of about a dozen areas. This included broad areas like artificial intelligence and machine learning but also extended to programming languages and data mining. All of the jobs we analyzed were posted on Indeed in the U.S. in mid-February 2020.

1. Deloitte

Multinational accounting and consulting giant Deloitte is the biggest game in town when it comes to AI and machine learning job openings, with 494 posted during the study period. Deloitte accounts for nearly 15% of AI job postings, about double the next-biggest hirer.

Deloitte offers the second-highest average annual salary ($126,020), and its job offerings were the most geographically diverse, with Houston, Boston, Denver, Chicago, Seattle, Cincinnati, Philadelphia, San Francisco and Los Angeles each being home to at least 15 Deloitte openings for AI experts.

2. Amazon

Amazon, the world’s largest retailer and fifth-largest company in the U.S., was seeking to hire 294 professionals in AI and machine learning, which equates to 8.6% of all openings in the space.

Amazon’s average annual salary figure is among the top half ($118,320), and the solid majority of Amazon’s job openings are, of course, in Seattle, the company’s home city. Silicon Valley is another hotspot for Amazon, with Palo Alto and Sunnyvale combining for 60 of Amazon’s AI posts.

3. Apple

Apple’s AI job openings were the third-largest at 146, about 4.3% of all openings we analyzed.

The Cupertino-based company did not offer enough details about salaries for us to calculate an average, but Santa Clara accounted for the vast majority of openings (119 total), followed by Seattle (21) and Austin (4).

4. Nvidia

Computer chip manufacturer Nvidia came in at No. 4 with a total of 79 job postings in artificial intelligence, machine learning and related areas. That equates to just under 2.5% of AI openings.

The company’s average salary ($108,660) was smack in the middle of the pack, and Santa Clara was home to the biggest number of openings (70), with Redmond, Washington accounting for 7 and Pittsburgh being home to a single AI opening at Nvidia.

5. JP Morgan Chase

JP Morgan Chase, the largest bank in the United States, came in fifth for AI job posts with 74, or about 2.2% of all AI posts.

Chase’s average salary figure, $107,900, puts it in the middle, and New York City was the most commonly listed location of job openings with 33, followed by Columbus, Ohio with 11 and Wilmington, Delaware with 8.

6. Microsoft

Microsoft was seeking to hire 46 experts in AI, which puts the company at No. 6, well behind Chase, with about 1.3% of all AI posts.

The company had the lowest average listed salary ($63,720), and the vast majority of its jobs were in Redmond (33).

7. Facebook

Facebook posted 45 openings in the AI space, or about 1.3% of all job posts that we studied.

The social media giant offers the second-lowest average salary ($93,840), and about half of Facebook’s openings were in Menlo Park, California, which is where the company has its headquarters.

8. Capital One

No. 8 Capital One posted 41 jobs that called for expertise in AI, which equates to 1.2% of all qualifying openings.

The bank and financial firm offers the highest average salary for its AI openings ($137,980), and almost all jobs were in Virginia, including McLean (31), Vienna (4) and Richmond (3).

9. Qualcomm

Chipmaker Qualcomm was No. 9 with a total of 26 job openings in machine learning and related areas, or less than 1% of all openings included in the study.

Qualcomm’s average annual salary was the third-lowest ($95,500), and almost all jobs the company listed were in San Diego.

10. Twitter

Just over two dozen AI jobs were posted by Twitter, or about 0.7%.

Twitter was offering AI professionals an average salary in the bottom half among the companies we studied ($102,800), and San Francisco was the site of most of the openings (18), followed by New York with 5.

Where Are the Jobs?

We’ve explored a bit about which companies are hiring in which cities, but what does the overall geographic picture look like for AI professionals? As one would expect, the West Coast is a hotbed of AI hiring, but that’s not to say that all experts should head west.

Openings by city, top 10

Seattle, WA 258
San Francisco, CA 201
New York, NY 183
Santa Clara, CA 121
Santa Clara Valley, CA 119
Boston, MA 92
Palo Alto, CA 79
San Diego, CA 77
San Jose, CA 77
McLean, VA 76

Conclusion

AI, machine learning and related technologies are projected to contribute nearly $16 trillion to the worldwide economy by 2030, but none of these disciplines will be useful without the hard work and support of qualified individuals, from programmers and engineers to analysts and researchers.

Can Artificial Intelligence Make a Difference In Combatting Coronavirus?

The mysterious coronavirus has been spreading quickly in the last few weeks. There has been a minimum of 305 fatalities and 14,300 people have the infection. (Forbes.com)

Last week, the World Health Organization (WHO) stated that the coronavirus is a global emergency. To give this perspective, it has already gone over the numbers of infects people during the SARS outbreak of 2002 and 2003 in China.

Many countries are working hard to put a stop to the virus. There have been several quarantines, lockdowns on large cities, travel limits, and faster research on vaccine development.

But is it possible that artificial intelligence (AI) could lend an assist? It actually already has.

BlueDot Is an AI Firm Already Helping With Pinpointing Virus

One example is BlueDot, which is a new venture-backed startup. The firm has designed a complex AI platform that processes billions of data pieces, such as from the air travel network around the world, to look for outbreaks.

With the coronavirus, BlueDot gave its first alert on December 31. This was before the CDC in America, which made an alert on Jan. 6.

BlueDot was the idea of Kamran Khan, an infectious disease doctor, and professor at the University of Toronto specializing in Medicine and Public Health. He was a mainline healthcare worker during the SARS outbreak 18 years ago.

Khan noted recently that his company is using natural language processing or NLP and machine learning to process large amounts of text data that is unstructured. It is currently processing 65 languages to track possible outbreaks of 100 disease, every 15 minutes, 24 hours per day. If this work was done by hand, it would probably require 100 people to do it right. The advanced data analytics allows health experts to focus their precious time and energy on how to respond to disease risks, instead of spending time on gathering and organizing vast amounts of information.

BlueDot will probably not be the only company to leverage AI to curb coronavirus. Here are other possibilities on the horizon:

Colleen Green, GM of Healthcare, DataRobot

Greene believes AI could predict many new cases by geographic location and which types of populations could be at the highest risk. This sort of technology may be used to warn travelers so that the most vulnerable populations can wear medical masks while they are on the road.

Vahid Behzadan, Computer Science Professor at the University of New Haven

AI can help with the improvement of optimization strategies. For example, some recent research on the use of machine learning to optimize social distancing or quarantining between cities, communities and countries to control the spread of epidemics could be groundbreaking. Also, my clinical research group is working with others in the field to develop methods to enhance vaccination strategies that leverage recent advances in AI, especially in reinforcement learning techniques.

Dr. Vincent Grasso, IPsoft Global Practice Lead for Healthcare and Life Sciences

When a disease outbreak happens, it is important to gather clinical-related information from patients and others that are involved, such as physiological state before and after the outbreak, logistical information about sites of exposure, and other vital information pertaining to the outbreak.

Deploying humans into such situations is difficult and expensive, especially if there are several outbreaks, or if they are in countries that do not have enough resources. Conversational computing can work as an extension of humans to get important information and would be a fantastic addition. Conventional computing can be bidirectional in that it can work with a patient and collect information. Or it can do the reverse – provide information based on plants that are modified or standardized based on assorted situational variations.

Also, working in a multilingual and multimodal manner would extend the conversational computing deliverable. In addition to this type of front end benefit, the data being collected from several sources such as text, voice, and medical devices and others are highly beneficial data points that can help doctors to learn how to fight an outbreak in the future more effectively.

Steve Bennet, Direction of Global Government Practice at SAS

AI can help with the coronavirus in several ways. It can predict hotspots around the globe where the virus may jump from animals to humans, known as a zoonotic virus. This can happen at exotic food markets that have poor health codes. Once an outbreak has been found, health officials can engage with artificial intelligence to learn how the virus is going to spread based on the conditions in the environment, people’s access to healthcare, and how it is being transmitted between people.

AI also can pinpoint and find commonalities within the localized outbreaks of the virus. Or, it can use micro-scale adverse health events that are unusual. The insights from such events can answer many of the unknowns about the nature of this virus.

When it comes to finding a cure for this virus, creating vaccines and antivirals is a long trial and error process. But the medical community has been successful in finding several vaccines for similar viruses in past years. So, using AI to find patterns from similar viruses and detecting attributes to look for to build a new vaccine gives medical researchers a better chance of getting lucky than if they were building one from scratch.

Don Woodlock, VP of HealthShare at InterSystems

As machine language improves, we can read billions of data points and clinical documents in patients’ medical records and find connections to patients that do or do not have the disease. The features of each patient that get the disease will pop out of the process of modeling, which can help us to find patients that have a higher risk.

Also, ML techniques can build a model or relationship between treatments that have been documented in medical records and later patient outcomes. These models can identify treatment options that are related to better outcomes and help guide the process of making effective clinical outlines.

6 Unexpected Uses of Artificial Intelligence

There is little doubt that AI or artificial intelligence is a technology that is transforming everything. There is so much that AI is changing, it is hard to guess the effects it will have in the next decade or two. That said, AI already is being used in innovative ways in many industries that are changing how things are being done. Below are some of the fascinating applications of AI that have occurred in the last few years. (Martechseries.com)

#1 Constructing Robot Bees to Pollinate Crops

Bees perform a vital ecological function, in particular for farmers who need pollination to germinate crops. As the bee population has been dropping in recent years, scientists are studying ways to imitate the vital work that bees perform. One solution they are working on is to create AI or robotic bees. These robots have tiny cameras, GPS and AI. This combination of software and hardware allows these tiny robots to find where crops are located and pollinate them as needed.

It is unclear at this time whether any company has actually developed robot bees yet, Walmart recently filed a patent application for methods and systems to pollinate crops with unmanned vehicles. (Businessinsider.com)

#2 Using Machine Learning and Sensors to Watch Crops and Predict Yields

It could be a few years before we see robotic bees pollinating crops, but some farmers are already using AI to boost productivity and yields. For instance, startup company PEAT is utilizing recognition technology to find any pests, plant diseases, or deficiencies in nutrients in the yield of a farmer. Farmers can easily upload photos to an app, whose algorithm analyzes the picture and shows detailed information on the crop and its possible disease. Also, farmers can select to put their crops under regular video surveillance to check for any diseases or other problems that could occur.

#3 Brew Beer That Tastes Better

Who isn’t interested in beer that tastes better? There are signs that AI can make for a better-tasting brew. The startup IntelligentX recently released four beers each with various profiles, such as black, golden, amber and pale. The idea is for beer customers to provide detailed feedback to a bot on Messenger. The feedback is sent to the brewer and used to enhance the recipe. By collecting this data, IntelligentX hopes to pinpoint trends early on. This will, in turn, affect the beers that the company will make in the future. Artificial intelligence can be used to transform the recipe over months and years to better adapt to changing consumer tastes.

#4 Writing Better Songs

It could be that the next big song you hear on the radio could be a product of artificial intelligence. As a matter of fact, it was already done! In 2017, Alex Da Kid’s song ‘Not Easy,’ performed with Ellie King, X Ambassadors, and Wiz Khalifa, rose on the Billboard and iTunes charts. While the song was being written, Alex Da Kid used Watson by IBM to review hit songs over the last five years and other important media content that could give him a better idea of the emotional temperature of the era so he could get his creative juices going.

After compiling all of this information to come up with a theme for the song, he then used Watson BEAT, a special algorithm that features machine learning to make various musical suggestions. This helped him to come up with a catchy sound for the song. While much of the work still has to be done by the songwriter, we can expect to see more artists using the vast analytical abilities of AI to target musical trends.

#5 Seeing Patient’s Health Worsening Before a Major Event Happens

AI has amazing predictive abilities. But rather than using it to determine if a song will make an artist a lot of money, or making an advertisement succeed, some healthcare companies are using it to make life-and-death impacts. Some companies are using AI to analyze the health data of patients to predict if a person is at risk of a serious medical problem, and then be able to stop it from occurring.

Healthcare startup Biofourmis can take information from a sensor that a patient wears and various sources to create unique biomarkers for each person that are monitored in real-time. If there is any change in the markers, this may be a sign that a serious health problem is about to happen. In this scenario, the AI is present in the sensors but also in the underlying technology that allows the company to decide what doctors and patients should be watching for.

#6 Improve Restaurant Customer Experience

AI has played a vital role already in boosting various features in customer experience software that is used by restaurants and hotels. Also, AI has been used to watch customer behavior and give an idea to eateries what has an influence on a customer positively or negatively. This has helped many restaurants to create a more positive image in the minds of many customers. (Martechseries.com)

AI in the restaurant business can also make it more convenient for managers to devise solutions that boost the consumer experience. Whether AI is used to analyze customer feedback or to come up with reports, restaurants are benefiting from using AI to enhance the customer experience.

#7 Reducing Restaurant Costs in Purchasing and Managing Inventories Effectively

Restaurants that use AI in their point of service systems are able to cut down costs with the analysis of analytical data that is accumulated by the system. It tells the management about which items are used the most and least in their inventory. Tracking and ordering products based on usage prevent items from being over-ordered or underutilized. This smart use of technology also prevents items from being kept beyond their expiration date.

AI has become firmly established in many types of industries today to help improve the customer experience and to help companies improve profits and become more streamlined. Where will AI take us in another 10 years? We will have to stay tuned in to see.

9 Ways Artificial Intelligence Is Transforming Real Estate

If you have ever asked Siri or Alexa to find your favorite piece of music, you are using artificial technology (AI). AI can be found everywhere these days, and it has started to transform every industry, including real estate.

In the past, the real estate industry was one of the last to adapt new technologies. But a recent study by Appfolio and John Burns Real Estate Consulting shows this trend is changing. Actually, 78% of property managers may consider their organization open to artificial intelligence. (Appfolio.com)

While the worry of machines replacing humans is a concern, the truth is that artificial intelligence just creates more time for humans to do the things they are best at.

AI is based on the idea that if a computer can be programmed to think like a human being, they can learn to perform tasks to increase our efficiency. They then can theoretically continue to learn over time to become more efficient. AI can reduce the amount of busywork we do and allow humans to use their expertise on work that requires more strategy and brainpower.

How can AI change the real estate industry? Here are nine ways.

AI Improves the Customer Experience

AI is an effective tool that can be used to unlock the potential of your business. It can boost team members’ capabilities and free their time to offer a better level of customer service. Renters place a high value on easy and fast communication, and conversational artificial intelligence technology can offer it. This gives your staff more time to provide exemplary customer service. By getting rid of the manual and repetitive tasks, AI gives more freedom for your property management and sales teams to focus on strategic tasks and forget deeper connections with customers.

Artificial intelligence technology can pull information from conversations, put that data into your property management software, and do tasks based on the information that has been acquired. This not only saves you time, but it also puts standard processes in place and eliminates the risk of human error. This better ensures an outstanding experience for customers, and data will be more accurate.

AI Streamlines Leasing, Enhances Conversion Rates

Quick, complete responses give you the edge when you are converting a lead to a lease. Zillow’s 2018 Consumer Housing Trends Report found that 71% of renters who asked about a listing expect to hear from the landlord or property manager in a day. But only 51% of renters said they received a response that quickly.

By using advanced conversational artificial intelligence technology that automates communication for leasing, you can get timely and accurate responses to every renter. The technology is intended to allow better operational efficiency during the leasing process to boost lead conversion, which reducing the load on leasing teams.

Conversational artificial intelligence technology can really impress renters by making it easier to book their walkthrough in less time from their cellphone, and by giving fast, complete answers to common questions. When they arrive at the place in person, leasing agents can focus on giving them warm, personal service.

Reducing Language Barriers

Global industries have thousands of documents in different languages. These documents in real estate have legally binding information, and when it involves a lease term, the industry must depend on local resources. When a real estate transaction is finalized, only the native speaker of the language can understand the contract terms. Not anymore.

New artificial intelligence technology can be trained to understand or speak any language. Through this innovative feature, AI can pull valuable information from global contracts and convert it to any language. The application of AI in real estate would allow agents to understand vital content without any language barriers. (Mashvisor.com)

Allow Virtual Access

Advancements in artificial intelligence are allowing for virtual tours of properties which can save a lot of time. Some virtual tours can even be accomplished today through the use of drones.

On the other hand, surveying land for construction is another plus with AI. With the use of drones, construction companies and buyers can look at land before they finalize their deals. This type of automation reduces the number of complexities and complications associated with real estate transactions.

Allows for Perfect Data and Actionable Insights

The decisions you make in real estate are only as good as the data you have. That goes for big and small decisions. You must have trust in the information that you are basing your business decisions on. By taking the human out of the data input part of the job, artificial intelligence lowers the chances of human error that comes with working too quickly with large amounts of data.

Advancements in AI technology in real estate are making it easier to collect information from all prospective renters. You can access real-time data and analytics on your rental portfolio. You also can accurately track leasing performance, the number of showings you have and conversion rates. Also, you will be able to make better-informed decisions with analytics and lead attribution.

Predicting Loan Defaults

Today’s real estate crowdfunding platforms can use AI to predict loan defaults, which boost investor profits. By making predictions on defaults, the process for risk assessment is more efficient. Platforms can focus on the most profitable investments while lowering the ones that are less profitable. (Becominghuman.ai)

Matching Deals

Real estate investors can establish their investment criteria and be alerted when a deal meets their parameters. For example, if an investor is interested in a first lien position on a commercial property with a 10% ROI, they can choose those parameters on their dashboard and obtain a property list that matches what they want, while also not including properties that do not meet their standards.

Construction Automation

Property developers and builders want to lower expenses and increase returns, just as all investors do. A new type of AI tools called proptech are being designed to help builders automate the purchase of materials so they can get the best materials from the best suppliers at the best price. By allowing the bots to handle the acquisition of materials, construction firms can reduce their expenses and boost profits with AI.

Property Management

AI can be used in your property management to predict and monitor when vital maintenance systems need to be replaced. The technology also is helpful in keeping up with rental trends in certain geographic areas, and raise rents on tenants when the lease is up. Other details of property management, such as expansion analysis and building automation can be done by property management firms to determine possible returns based on inputs that affect rents, expenses, and profits in rental properties.

AI and machine learning are being used more often today in every area of real estate. But its expanding use in real estate investing allows investors, builders, and property managers to access more effective ways to increase returns, control costs, and manage risk with automated systems that are based on the concerns of individual investors.

Does the greater us of AI spell trouble for real estate professionals and their employment? It should not lead to problems right away, but it will definitely lead to some changes. For the most part, AI will help real estate professionals and investors save money and time in terms of serving the client and adding operational efficiency.

Mesothelioma and Breast Cancer Patients See Benefits from Artificial Intelligence

The concept of artificial intelligence or AI has been around for decades, but it is only starting to reach some of its high-powered capabilities today. While humans are not yet entirely dependent on AI, it is being used to help society in areas such as email assistance to groundbreaking changes such as self-driving vehicles. (Worldhealth.net)

In addition, artificial intelligence is having significant effects on the medical field. It has proven its ability, for example, to analyze radiology images and support doctors in the detection of tumors. AI has particularly promising possibilities for patients who have cancers that are especially hard to diagnose.

In the last few years, AI’s evolution is being directed to providing assistance in mesothelioma research and healthcare. Clinical studies have shown that artificial intelligence can play an important role in improving preliminary tests for cancer. For mesothelioma patients, artificial intelligence may be a vital component to finding an earlier diagnosis. This early diagnosis could lead to earlier, targeted treatment, which could improve life expectancy.

Mesothelioma Is One of the Toughest Cancers

One of the challenges with mesothelioma is that it can take 20 years or longer to show up in the body. There are four stages of the disease, and if it is diagnosed in a later stage, the patient’s prognosis is poor. Most people diagnosed in stage 3 or 4 only live 16 months. Mesothelioma is caused by asbestos exposure in industrial and manufacturing applications. Unfortunately, people may not be aware they have the cancer until it is too late. Some doctors misdiagnose mesothelioma as lung cancer, which means it is not properly treated until it is too late.

Artificial Intelligence Can Allow for Earlier Diagnosis

AI is of great value to mesothelioma patients. It can offer a clear, earlier diagnosis that allows them to start treatment sooner. In earlier cases, diagnosis was later, and it left patients with fewer treatment options. AI offers hope for people who would have otherwise short life expectancies. While AI is not the only possibility, its use if it is proven reliable, can transform the prognosis of cancer patients around the world.

Other cancers, including breast and lung cancer, can also see benefits from artificial intelligence with early detection. Once the cancer has been detected, doctors can start with treatments before the cancer becomes more aggressive.

Deep-Learning Program Launched in 2019

In October 2019, researchers came up with a deep-learning program called MesoNet to identify possible mesothelioma patients early on. The program scanned tissue samples with this technology to understand who would respond best to certain cancer treatments. Radiation therapy, immunotherapy, are best used when doctors can address the cancer before it spreads to other areas, such as those where operations are ineffective. AI, in this case, was used to pinpoint a new tumor and was able to connect it to effective treatment and prognosis.

The model has been tested and verified by doctors at the Centre Leon Berard Cancer Institute in Lyon, France. MesoNet can not only predict mesothelioma in patients but can also identify new biomarkers in the stromal parts of the tumor microenvironment that were most predictive of survival.

Before these breakthroughs, machine learning was put into practice to provide advances in artificial intelligence. As AI has made a large impact in radiology image analysis, this technology has great potential because it can process images faster and with greater accuracy than doctors.

Artificial Intelligence Shows Hope for Breast Cancer Patients

AI is being introduced into diagnosing breast cancer in the early stages and the outlook is promising. Ultrasound elastography is a new diagnosis technique that tests how stiff breast tissue is. It does this be vibrating the tissue, which causes waves. This wave leads to distortions in the ultrasound scan, which highlights the parts of the breast where properties differ from the other tissue. (Medicalnewstoday.com)

From this information a doctor may be able to find if a lesion has cancer or is benign. While this technique does have a lot of potential, analyzing the results of this technique takes a lot of time, involves many steps, and requires the solving of complex problems.

Last year, a group of researchers at the University of Southern California in Los Angeles tried to create an algorithm that could lower the steps that are required to draw information from the images. The results were published in the journal Computer Methods in Applied Mechanics and Engineering.

Researchers wanted to see if an algorithm could be trained to tell the difference between benign and malignant lesions in breast scans. They tried to do this by teaching the algorithm with synthetic data instead of genuine scans.

Synthetic Data

When it was asked why the team was using synthetic data, the lead doctor on the study said it all comes down to how available real-world data is. He noted that in medical images, you are lucky to have access to 1,000 images. In this type of situation, where data is rare, these techniques are critical. (Medicannewstoday.com)

The clinical researchers trained their algorithm, which they call a deep convolutional neural network, using 12,000 synthetic images. By the end of the study, the algorithm had 100% accuracy on synthetic images. Next, they wanted to try scans in real life. They had only 10 scans to access. Half of them showed malignant lesions and the other half had benign lesions.

They had an accuracy rate of 80%. They then continued to refine the algorithm by using real-world images. While 80% is a good amount, it is not sufficient But this is just the beginning of the process. The scientists thought if they trained the algorithm on real data, it could have shown more accuracy. The researchers also noted that the test was too small to test the real capabilities of the system.

This experiment showed the potential value of using AI to diagnose certain forms of breast cancer. But the leaders of the study did not think that AI will ever fully replace human operators. While these algorithms do have a major role to play, human operators will always be needed to make final conclusions.

But researchers hope they can expand this method to diagnose other forms of cancer, such as lung cancer and mesothelioma. When a tumor starts to grow, it changes the behavior of the tissue, so it should be possible to chart the differences and train the algorithm to identify them.

DeepMind and Google Health Announce Collaboration

DeepMind and Google Health are collaborating to devise an artificial intelligence algorithm that is more accurate to find early breast cancer than human radiologists. Another form of artificial intelligence called an artificial immune system, can help doctors to find malignant pleural mesothelioma in some patients. The study showed 97% accuracy and was better than the current algorithms for cancer diagnosis.

As with any disease, timing is of greatest importance. Artificial intelligence is unlikely to over replace human judgment, but it can help doctors to reach higher levels of cancer remission. If artificial intelligence can be used in oncology, whether it is for supplementary image analysis scanning or by finding possible cancer patients to specialize treatment, this can be a way for some patients to receive the best possible care.

China Is About to Become the 1st Artificial Intelligence Superpower

Forbes reported recently that China is on the way to being the first global superpower for AI or artificial intelligence. The nation has the most aggressive and ambitious artificial intelligence strategy of all countries and offers the most financial resources around the world for its implementation. (Forbes.com)

China is able to combine a huge amount of data with top talent, research, companies, and capital to build the best AI ecosystem on the planet.

In 2017, the State Council of the People’s Republic of China released its Artificial Intelligence Development Plan. (FLIA.org). The strategy is part of a larger ‘Made in China’ 2025 plan that is going to be linked to the digital Silk Road.

With these big AI plans, China wants to become the largest economic power in the world and offer people with enough prosperity that is guaranteed by a stable political system. Also, China wants to ensure that military, economic, and diplomatic interests are safeguarded.

AI To Protect and Enforce China’s Interests

Experts say that AI will play a key role in this. Artificial intelligence is intended to connect and upgrade all Chinese industry by 2025. AI also will produce goods and oversee companies, while providing balance to supply and demand.

Also, AI is planned to help the national government control and monitor the population. Artificial intelligence is going to be used to protect digital and military interests. It also will allow the population to live a safe and good life.

System Will Have Central Strategy But Local Implementation

China is going for a centrally controlled AI strategy with local implementation. Goals and values are set from the national government and financial resources are made available to localities. At the local level, regional administrations will compete for the new AI technology and resources.

The result, the Chinese government says, will be a national and regional administrative state that works with investors, research, and industry to build a dynamic and successful artificial intelligence ecosystem.

The implementation of this strategy will vary from region to region in China. Cities such as Shanghai and Tianjin already have billions of dollars in Venture Capital funds for AI and had entire islands and districts set up for new artificial intelligence companies, other provinces are still learning and developing their systems.

AI Can Be a Career Engine

Overall, it appears that China is doing many things right to grow an artificial intelligence infrastructure. There are resources, goals, and legal frameworks being used with local freedom to adapt, so the AI industry is growing quickly. At the same time, the national government offers incentives for politicians and administrators to assert themselves in the artificial intelligence industry, and to recommend their availability for higher-level tasks.

To make this happen, the national government is taking an analytical approach and is aware of its strengths and weaknesses. There have been hundreds of new AI professorships set up. There also have been hundreds of thousands of study places established.

China has an efficient and mature start-up ecosystem where younger AI companies are also growing. There is enough capital from the private and state sectors to establish, scale and grow artificial intelligence startups in China.

Chinese Government Demands and Promotes

New companies also get government contracts, tax breaks, and offices in artificial intelligence clusters if they want them.

China’s government also is working with well-known digital companies such as Baidu, Alibaba, and Tencent. These orientations from a strategic standpoint are helped by the central government, as well as the collection and exchange of data in the companies.

So, China now has the biggest capital market for AI startups. It also is now publishing the most AI research papers and has generous data regulation. Further, it is training the most artificial intelligence talent.

However, China is not particularly diverse or creative and has few strategic partners. That is why several Chinese government agencies have been given the mandate to get talent from Europe and to build better relationships with partners in Europe.

The Social Core of China

A good example of the application and implementation of AI is what is known as the Social Core. It reflects the concept of controlling and managing society with machines rather than people.

Every nation has cultural norms, laws, social morals and social agreements. The courts, politicians, police, media, and citizens are involved in a regular dialogue, which determines what society says is right or wrong. In China, this dialogue is largely being taken over by machines. Machines are deciding what correct and incorrect behavior is.

The Social Score system collects many types of data about companies and citizens, and sorts, analyzes, interprets and implements actions based on this data.

In the real world, this means if you wait at a light that is red, you get + points. If you pay your taxes on time, you get more + points. If you play by the rules, you get more + points.

If you have a strong Social Score, you will get unsolicited benefits for good behavior. For example, you will get more efficient visa application processing and more travel freedoms. When you are dating online, your profile will get more prominence. Banks also will offer you lower interest rates. People with a higher Social Score get better job offers.

But people who run red lights, pay bills late, or criticizes the government in social media gets – or negative points. A poor Social Score reduces the chances of reproduction, making money, or being able to travel.

Is This Freedom or Security?

The scenario above is not fiction. It is already being used in some areas of China today. Also, Singapore is testing its own AI-driven societal monitoring systems with similar goals.

For Americans, the Social Score raises many questions:

  • Who does the monitoring of the score? Who is importing the data? Who is training the system?
  • How do you integrate the ethical debates and moral consensus of a society?
  • Which authorities are monitoring this system to prevent abuse of power?
  • How do you ensure privacy of people and companies?

Gathering data and establishing administrative systems to ensure freedom, protection, and security are good tools for states. But there are worries that all of this use of AI will harm civil liberties, and this is a serious matter in the US, if not in China.

Companies Are Now Using AI To Help With Hiring, But You Can Beat It

It has been argued that artificial intelligence can free the processing of hiring from biases and prejudices. In theory, there can be a totally neutral system that looks at job candidates and chooses the best one, no matter their gender, race or other characteristics. (Inc.com)

It sounds good, but so far, it has been largely a failure, some AI experts say. AI works only as well as the programmers make it. And programmers are human beings with flaws.

Amazon’s AI System Discriminated Against Women

Amazon, which has a lot of money to spend, actually had to dump its AI recruiting tool because the system was discriminating against women. Machine learning specialists at the online retail giant discovered that their new recruiting engine was being unfair to all women candidates. (Reuters.com)

The machine learning team had been designing computer programs since 2014 to review resumes with the idea of automating the search for the best talent. Automation has been vital to Amazon’s dominance of the e-commerce world, whether it is in warehouses or making pricing decisions. The company’s hiring tool was using AI to assign scores to job candidates from 1-5 stars, sort of the way shoppers rate products on the website.

According to one of the specialists on the projects, everyone involved in the project wanted there to be an AI engine where you could put in 100 resumes and it would give you the top five, and then those people would be hired.

But by 2015, Amazon became aware that the new AI system was not rating job applicants for tech jobs in a way that was gender-neutral. This was because the computer models were trained to check applicants by looking at patterns in resumes that were submitted to Amazon over 10 years. Most of them came from men, which reflects how much men dominate the technical world.

Amazon’s AI system learned that male candidates were more desirable. It was penalizing resumes that including ‘women’ or ‘women’s,’ such as women’s debate club captain. It also penalized the resumes of people who graduated from all-women’s colleges, according to some on the machine learning team.

Amazon performed edits to the programs so they would be neutral to female-oriented terms. But that was not a guarantee that the system would not figure out another way to sort candidates in a discriminatory way.

Amazon eventually had to disband the machine learning team by the end of 2018 because executives did not think the project would work out. Amazon’s recruiters did look at recommendations that the tool generated, but they would never rely on those rankings alone.

Amazon will not say much more about the challenges of the technology, but it has said in media reports that the AI tool was never used by recruiters to evaluate job candidates.

Amazon Experiment Shows Limits of AI

This Amazon experiment shows some of the limits of machine learning. It also is serving as a good lesson to large companies that want to automate the hiring process. Approximately 55% of HR managers in the US report that AI will be a routine part of their job in the next five years, according to a Careerbuilder survey in 2017.

Employers have for years wanted to use technology to make hiring easy and reduce reliance on fallible human beings. But according to many computer scientists, there is much work to be done before an AI system will be able to be relied on to hire candidates.

Also, HireVue is facing criticism from civil rights groups over its systems used for hiring. According to The Washington Post, the system uses video interviews to review hundreds of thousands of data points related to how a person speaks, the words they use, and facial movements. The system creates a computer estimate of the person’s behaviors and skills, which includes the willingness to learn and their personal stability.

Learn How to Beat the AI Bots

These types of AI programs are what are encouraging several South Korean consultants to create new businesses to teach people how to beat AI bots in the hiring process.

Gaming the system has been tried for as long as people have been trying to get hired for jobs. There are tons of articles online that tell you how to give good answers to standard interview questions or tell you how important a firm handshake is. This is not much different than the training these consultants are providing. But the difference here is you are trying to convince an AI-driven machine to hire you, not a human being.

That is what makes this type of training so important. While it is generally true that firm handshakes are important, you can run into an interviewer who prefers a dead-fish handshake. In that case, the advice would hurt you and not help you. But if two companies are using the same software, the information from these South Korean consultants will help you no matter who the human hiring manager is.

The goal is to take all human biases out of the interviewing process. But biases are still in AI. It is just that all jobs require you to overcome identical preferences. This means it will be easier to beat the system. After the consultants have figured out what the algorithms want, they can train you to respond in preferable ways that could get you consideration by the hiring manager.

This type of training can level the playing field, but people who can afford the consultants’ training programs will do best in job interviews. Interviewers are already known to discriminate based on class, so the problem is not really solved at all.

Can AI eventually make hiring less discriminatory? It is possible, many believe. But, as these South Korean consultants understand, anytime there is a system made by human beings, there is a way it can be beaten. All humans are fallible, but everyone knows that. AI allows some people to think that the process has no biases, but that is not the case. It just makes the bias consistent.

Warner Brothers Using AI To Predict Box Office Hits

Warner Brothers recently announced it would use artificial intelligence (AI) to help predict which films will be successful. The new AI platform will let them test profitability outcomes that depend on varying factors such as casting, release date, and distributor.

Warner Brother International has signed a deal with LA company Cinelytic to use their AI project management system. (News.Sky.com)

The forecasting tool was first used in 2019 and offers unique insights to the film industry, according to the company’s website. It claims their platform can predict box office returns using a variety of data. One function allows the user to see how casting various actors in different roles would affect how much money the movie makes.

Cinelytic uses the example of Gal Gadot in Wonder Woman as its demonstration image. It says it uses a trademarked economic scoring system known as TalentScores. It states the system ranks talent by their economic effects across the film industry, including genre, media type, and key territories.

Different film distributors and release dates also can be tried out to see what the results would be.

Cinelytic Was Founded in 2015 by Film Producer and Nasa Employee

The company was co-founded four years ago by film producer Tobias Queisser and ex-NASA employee Dev Sen. The company has been performing tests on its platform for three years.

Queisser, who went to college in London, told the media recently that their system can calculate in a few seconds what it once took days to assess by a person when it comes to evaluating the potential profits of a film, or the worth of having a certain star in a film.

Although the capabilities of the AI platform are substantial, he says it is not meant to be a complete replacement for humans as far as the creative spark that is necessary to make good films. Artificial intelligence is good for performing certain tasks, but AI cannot make creative decisions, Queisser said.

He added that AI is effective at crunching numbers and breaking down large data sets and showing patterns that are not clear to humans. But for the critical creative decision making, you still have to have gut instinct and experience that only humans possess.

When the new partnership was announced, the senior vice president for Warner Brothers, Tonis Kiis, said the company is excited to use Cinelytic’s cutting edge AI system. In the film industry, tough decisions are made every day that affect what and how the company produces and delivers films to theaters across the globe. The more precise the data is, the better the company will be able to engage audiences.

Some AI Experts Are Skeptical

Many experts in artificial intelligence are skeptical that algorithms can make predictions in a business as complex as filmmaking. Because machine learning tools are taught historical data, they are often conservative, focusing on patterns that brought success in the past instead of predicting what would excite audiences in the future. Scientific studies also say that algorithms only produce minor predictive gains. (Theverge.com)

What Cinelytic’s AI System Means for the Future of Film

In a recent interview, Queisser said his software was providing the film industry a Netflix level of insight. He also said the AI system cannot detect the next big film trend, but it collects data in a range. Cinelytic constructs predictive models based on rating, talent, genre and release size. He said they give the system a base scenario, and what should happen. The platform analyzes risk levels to guide users.

Cinelytic has promised the software will not make any impositions on creative types. The founder said that new technology in the film industry is often rejected but the platform does not tell a screenwriter what a good story is. Rather, he has positioned the software as being for bigger film studies, noting that if a film is assembled, the core part of it is known as a package, which is the script. The talent around the film needs to know if the package is worth $20 million $50 million, $100 million or more.

Some film industry professionals wonder if technology should be making decisions that surround creative ones. Queisser stressed that his system would be a tool for film studios, but not the entire decision-making process about a movie. It would work with executives on the decisions they need to make about certain films.

But the history of Hollywood has always leaned in the direction of big studios following popular trends. It is only in recent years that some large studies have broken trends, such as with Joker. That they say that movie would not have happened if their cinematic universe had competing earnings.

In just seven weeks after its release, Avengers: Infinity War brought in $660 million in the US, more than Justice League’s global sales. Insiders say that Queisser’s description of his product does not inspire much confidence that blockbusters in the future will be much different from each other.

Several Movie Studios Plan to Use Cinelytic

The studios that have signed up for Cinelytic are somewhat of a mixed bag. One of them is Ingenius Media with the popular film Wind River, a murder mystery movie with a budget of only $11 million. It had an 88% Rotten Tomatoes score and earnings of $45 million.

Another film company is STX, which had a mediocre 2019 with Playmobil and Uglydolls. The latter had a budget of $45 million and only made $32 million. Some of these studios may be using Cinelytic to recoup some losses by going with safer movies. With Cinelytic, studios should have a better idea of what audiences want to see.

In a recent interview, Queisser said that with the platform’s pattern recognition technology, it is able to see which actor works the best from a data-driven direction. But that does not sound great for diversity in movies.

Some worry that with Cinelytic’s AI guiding companies, executives could push creatives and minorities away in favor of just making money. But the idea that diversity is in opposition to making money is an old idea. Black Panther ranks among the highest-grossing films for Marvel. The hope is that executives will use Cinelytic as a guide but will still allow for creativity and diversity in films to come through.

Defense Department Launches Artificial Intelligence (AI) Strategy

The Defense Department launched its artificial intelligence (AI) strategy (Media.defense.gov) in February 2019, in concert with a White House announcement that created the American Artificial Intelligence Strategy. (Defense.gov)

The executive order from the president is viewed as important for the US to remain a leader in artificial intelligence, according to Dana Deasy, the Department of Defense’s chief information officer. He said that order will increase the prosperity of the country and boost national security.

Deasy and Air Force Lieutenant Jack Shanahan, the first director of the Department’s Artificial Intelligence Center, talked about the strategy’s launch with DC journalists.

Overview of the National Defense Strategy

The National Defense Strategy (Dod.defense.gov) recognizes that the United States’ global landscape is evolving quickly, with China and Russia making big investments to modernize their armed forces. That investment includes a high level of funding for AI capabilities. The DOD artificial intelligence strategy will directly support all aspects of the National Defense Strategy.

AI is in the process of transforming every industry and will affect every aspect of the Department of Defense, including operations, training, sustainment, recruiting, healthcare, force protection, and more. With AI being applied to defense, the US has the chance to boost support for and protection of America’s service members, safeguard citizens, and defend US allies and partners.

As the AI strategy states, the US, in partnership with its allies need to adopt AI to enhance its strategic position so it can win on future battlefields and maintain a free and open international order.

The Department will incorporate artificial intelligence into operations and decision-making to cut the risk to service members in the field and provide a military advantage. Artificial intelligence can help to maintain equipment better, reduce the cost of operations, and boost readiness. Using artificial intelligence also offers the potential to boost the implementation of other initiatives, such as The Law of War. By enhancing the accuracy of our military assessments and increasing mission precision, AI can cut the risk of civilian casualties in US military operations.

Speed and Agility Are Vital

Boosting speed and agility is a major focus of the AI strategy, the chief information officer told reporters when the strategy was announced. Those factors will be part of all Department of Defense artificial intelligence capabilities across all DOD missions.

Deasy noted that the success of America’s artificial intelligence initiatives relies on strong relationships with our allies and partners. Industry, interagency, allies, and the academic community all play a critical role in executing the nation’s artificial intelligence strategy.

He also noted that it is important to stress the importance that the academic community has in the success of the artificial intelligence initiative. Young, intelligent minds will continue to bring good ideas to the table, and look at the problems with AI from a different perspective. The future success of both the Department and the country depends on using young minds, capturing their imagination and interest so they eventually want to work at DOD on AI-related efforts.

Reform of Department of Defense Business

The last aspect of the National Defense Strategy centers of reform, Deasy told the journalists. The Joint Artificial Intelligence Center (JAIC) will allow many new opportunities to reform the business processes of the Department of Defense. Efficient, smart automation is just one area that could improve effectiveness and efficiency.

AI in the Department will use a foundation based on the enterprise cloud. This will boost efficiencies across the Department of Defense. He also noted that the Department of Defense will stress responsibility and use of AI through guidance and vision principles for using artificial intelligence in a lawful, safe and ethical way.

Joint Artificial Intelligence Center: A Focal Point of Artificial Intelligence

It is difficult to overstate how important it is to operationalize artificial intelligence across the Department of Defense and to do it with a proper sense of urgency. The DOD AI strategy is relevant for the entire Department, and the JAIC is a major part of the initiative. The JAIC was founded as a response to the 2019 National Defense Authorization Act. It has provided a common vision, mission, and focus to drive Department-wide artificial intelligence capability delivery.

Themes of the Artificial Intelligence Mission

The JAIC has several vital missions:

  • The effort to speed the delivery and adoption of artificial intelligence capabilities across the Department of Defense. This underscores how important it is to transition from research and development to operational-fielded capabilities. The JAIC is going to operate across the entire artificial intelligence application lifecycle, with an emphasis on execution in the near term and adoption of AI.
  • Set up a common foundation for scaling the impact of artificial intelligence. One of the most important contributions of the JAIC over the long haul is to establish a foundation that is enabled by enterprise cloud with a focus on shared data repositories for frameworks, useable tools, and cloud services.
  • To synchronize Department artificial intelligence activities, AI and machine learning projects are being undertaken across the department. It is key to ensure full alignment with the NDS.
  • The initiative to attract a world-class, and talented artificial intelligence team.

There currently are two pilot programs that are US mission initiatives, which are broad, cross-cutting artificial intelligence challenges. They comprise humanitarian assistance and preventive maintenance and disaster relief. The initial capabilities in these areas were delivered over the rest of 2019.

While the JAIC is in its early stages, it is starting to work with the US Cyber Command on a national mission initiative related to the space program. Everything that is done in JAIC is going to focus on boosting relationships with academia, industry, allies, and international partners of the United States. Within the Department of Defense, staff will work closely with the armed forces, Joint Staff, combatant commands, agencies, and components.

The mission of JAIC complements the executive order the president signed last February, but there is a lot of work ahead, and this will continue in the next several years.

Tech Companies Are Shelling Out Big Salaries for Scarce AI Talent

Silicon Valley’s start-up companies have always had an advantage in recruiting over tech giants. Start-ups say, take a chance on us and we will give you ownership of the firm that can make you rich if we succeed.

Now the technology industry’s race to embrace AI could make that advantage moot, at least for the workers who have knowledge and skills in AI. (Nytimes.com)

Tech’s largest companies are putting big bets on artificial intelligence, as they bank on technologies such as face-scanning smartphones, computerized healthcare, and autonomous vehicles. As they are going down this high tech path, the big tech companies are paying big salaries to AI professionals that are extremely high, even in an industry that has routinely giving big bucks to top talent.

AI Workers With Limited Experience Can Garner Salaries of $300K or More

Some AI specialists, who usually have a Ph.D. just out of school, and sometimes people with less education and three or five years of experience can get paid from $300,000 to $500,000 per year in salary and stock options, according to a survey of nine people who are working for large tech companies. All of them asked for anonymity because they do not want to harm their career prospects.

Some of the best-known names in the artificial intelligence field get compensation in salary and shares of stock that total several million dollars over four or five years. Then they can renew or negotiate a new deal, much like professional athletes.

At the high end are the executives with years of experience managing AI projects. In a recent court filing, Google stated that one of the leaders of its self-driving car division, took home more than $120 million in incentives before he went to Uber last year.

Salaries are rising so fast that some say the technology industry should have a salary cap as the NFL does. One of the hiring managers at Microsoft says a salary cap on AI professionals would make life a lot easier.

Many Reasons for the High AI Salaries

There are many reasons salaries for AI professionals are rising. The automotive industry is competing with Silicon Valley for the same workers who can help to build and design self-driving cars. Big companies such as Facebook and Google have a lot of money to spend and problems they believe AI can fix, such as creating digital assistants for smartphones, smart home gadgets, and spotting content that is offensive.

The biggest reason is there is a shortage of AI talent. The tech giants are trying to land as many of those workers as they can. Solving difficult AI problems is not easy, like building a routine smartphone app. On the entire planet, fewer than 10,000 people have the skills to work on serious AI research, according to an analysis by Element AI, a lab in Montreal.

While what we are seeing with these increasing salaries may not be good for society, these companies are behaving rationally, according to Andrew Moore, dean of computer science at Carnegie Mellon University. The companies are anxious to grab this small number of people who can work on AI technology.

One AI Lab With 400 Employees Has $138 Million in Salaries

Costs at an artificial intelligence laboratory called DeepMind, which was bought by Google for $650 million in 2014, when it employed 50 people, show the spiraling salary problem. In 2016, it was reported by the company that its staff costs for 400 employees were $138 million, or $345,000 per employee. Those types of salaries are hard to compete with if you are a smaller company.

Current AI Research Based on Deep Neural Networks

The cutting edge of AI research is based on several mathematical techniques called deep neural networks. These networks are algorithms that can learn a task by itself by crunching data. For example, by reviewing millions of dog photos looking for patterns, a neural network can teach itself to recognize a dog. This mathematical concept goes back to the 1950s, but it was on the fringes of industry and academia until 2012 or so. (Wired.com)

By 2013, Facebook, Google, and a few other major players began to recruit the few AI researchers who are experts in these techniques. Neural networks can now help recognize the faces that are posted on Facebook. They also can identify commands that are spoken into digital assistants such as the Amazon Echo, and can immediately translate foreign languages in the Skype service owned by Microsoft.

Those same mathematical algorithms are being used to improve the performance of self-driving cars and develop hospital technology that can identify disease and illness in medical scans. Digital assistants are being created that can not just recognize spoken words but understand what they mean.

Tech Companies Are Hiring Professors and Reducing Ability to Teach AI to Students

With so few AI experts available, large tech companies are also moving to hire the best and brightest who work at colleges and universities. In the process, they are putting a limit on the number of instructors who can teach AI and machine learning.

For example, Uber hired 40 people from Carnegie Mellon a few years ago, which ran a revolutionary AI program to work on self-driving cars. Over the last four years, four of the best known AI researchers at universities have left to take high-paying jobs in the private sector. At the University of Washington, six of 20 AI professors are working for private companies on leave.

But some professors are finding ways to compromise. One professor at the University of Washington turned down a $180,000 position at Google. He chose a post at the Allen Institute that would let him continue teaching. He said there are many faculty who do this by splitting their time between academia and industry. While the salaries are much higher in the private sector, some professors choose to split their time because they really care about teaching the next generation of AI professionals.

Some Tech Companies Are Teaching AI Skills to Current Employees

To increase the number of AI-skilled engineers, Google and Facebook have been running classes to teach machine learning and deep learning to current workers. These companies say that learning basic deep learning concepts is not difficult, and does not require much beyond high school math. But some expertise requires more difficult math and intuitive talent that some say is like a dark art. Very specific knowledge is needed to create technologies such as self-driving cars, healthcare services, and robotics.

To keep pace, smaller firms look for talent in unorthodox places. Some hire astronomers and physicists who have strong math skills. Other start-ups look for workers in Eastern Europe, Asia and others where lower wages prevail.

Overall, demand for AI professionals is outweighing supply, so people with AI skills and expertise can continue to expect people to line up to pay them very well for their talents.

Artificial Intelligence Is Changing Manufacturing

Over the last 50 years, some experts believe that parts manufacturing became complacent and failed to embrace innovation. That is why the parts manufacturing industry has struggled to keep pace with the fast pace of digital transformation today.

As Industry 4.0 technologies are growing, inefficient, and sluggish manufacturers are starting to feel the pinch. They have a choice of innovating or becoming outdated. This is being accentuated by the speed at which digital manufacturing is expanding and growing. (Supplychaindrive.com)

Today, digital manufacturing is starting to use technology to improve how parts are produced. The foundation of this industry-wide evolution is artificial intelligence (AI) and machine learning. Many believe that AI is one of the most disruptive technologies today.

AI and Machine Learning Worth $70 Billion in 2020

Some reports suggest that artificial intelligence and machine learning technologies are worth $70 billion in 2020. The machine learning industry could transform all types of business operations in all sectors. A report from NewVantage Partners in 2019 stated that 90% of C-suite executives believe investment in big data and AI is needed to develop and maintain competitive businesses. While there are many examples of AI benefits in many businesses, AI-equipped manufacturing businesses are reported as the most added value. (Mckinsey.com)

That is why Mckinsey expects the manufacturing industry to be one of the leaders in AI going forward, noting in its report that manufacturing is on the cusp of a revolution where AI applications will change end-to-end value chains with major shifts in demand. But much of the industry continues to struggle with getting started with cutting edge digitization. While most decision-makers think it is important for their companies to use AI, most have yet to get beyond pilot programs.

McKinsey also reports that most organizations in most sectors have begun to adopt AI in their businesses. A recent survey found that 47% of respondents said their companies are using at least one AI capability in their manufacturing processes, compared to only 20% of respondents in 2017. Another 30% said their companies were piloting AI. Still, McKinsey states there is much more potential to use AI across supply chains. Today only 21% of companies are using AI in multiple parts of their business. So far, investments in AI are still quite a small fraction of companies’ overall spending on technologies. More than 58% of respondents to the recent McKinsey survey said that only 1/10 of their firms’ digital budgets went toward AI.

The most common AI capabilities that have been deployed in manufacturing companies so far are:

  • Robotic process automation
  • Computer vision
  • Machine learning

For each of these, 20% said their firms had embedded these technologies into their manufacturing processes. Physical robotics and autonomous vehicles are less commonly deployed, largely because they are only useful for companies in specialties where there is a definite application for them.

Mentality Is the Major Issue Holding Back Manufacturing Companies

According to PwC, the biggest issue holding back manufacturing companies in the implementation of AI is their mentality. Many heavy manufacturing companies have a conventional engineering mindset that is averse to taking risks and is less about major internal process innovations.

That said, the benefits for manufacturing companies that use AI and machine learning in their daily processes could be massive. Machine learning, when it is used to assist human workers, can increase labor productivity by 40% and create major increases across value chains. For manufacturing, this is more than simply primary business functions, which is the actual manufacturing process. AI and machine learning also can benefit sourcing, maintenance and supply communications.

Machine Learning Can Be Used To Generate Quotes

A good example is using machine learning to improve the quote generation process. Usually, generating quotes can take up to three weeks to get approved. When an engineer is doing work on a product and needs a part for it, reducing the time for iteration is a big advantage for innovation. The period can be longer for many reasons, but human workers play a significant role.

Say you are making a part, and you choose three factories around the globe that can deliver the product you want. Your part is essential to the success of the product, but you are a smaller client, so the manufacturer does not give the production of your part priority. You may wait weeks for the quotes to be returned, and when they do, one is denied and the other two are too expensive.

Some companies may give you a higher price to encourage you to take your business elsewhere because they think making your part is not financially worth it. Others may think this is what the part is really worth. Whatever the case, the discrepancies are significant and are usually based on human evaluation of the situation.

Machine Learning Algorithm Can Greatly Increase Speed of Quote Process

When human bias is combined with the communication cycle that can take weeks, inefficiencies can pile up. But take the above example and use a machine learning algorithm to the process. Rather than the part quote getting printed out and left on a desk, when you upload the part and select your materials, the system would analyze the part right away and determine what it is worth and whether making it for a profit is possible.

With the major time and money-saving possibilities, the use of machine learning with manufacturing will become more important to keep businesses competitive and keep the sector growing.

While large manufacturers may be ok with operating behind the times, this will not work forever. The benefits of using AI and machine learning are major for manufacturing because they are embracing the latest innovation and technology. For those companies that choose not to, the scenario could resemble the one that faces Blockbuster and Netflix a decade ago. In the long run, if manufacturers really believe in giving engineers more power, the decision will be simple to make.

China Boosting Tech Education to Become AI Leader

China has been making significant efforts in recent years to become a world leader in artificial intelligence (AI) by 2030. For example, the Chinese government has focused heavily on teaching young people to code in the last several years. This is especially the case as China is trying to close the gap in its workforce that works in technology, especially in AI. (NBCnews.com)

In November, the education ministry in China updated its curriculum to include materials about big data, AI, coding, and quantum computing. Approximately 25% of the 422-page recommended reading list features materials about math, science, chemistry, aerospace, medicine, and AI.

According to Vita Zhou, an eight-year-old who is the poster child for China’s efforts to become a leader in AI and high tech, learning to code is ‘not that easy, but also is not that difficult, at least not as difficult as you have imagined.’ Vita has learned Swift, Scratch, and C++ at only eight years old.

From his apartment in Shangai, Vita is the host of training videos for other Chinese children on how to code for AI. He now has 80,000 followers on Bilibili, a Chinese streaming website. Some of his videos have had more than 1.3 million views. Vita has gotten attention from Apple CEO Tim Cook, who sent him birthday wishes on Weibo, which is similar to Twitter.

China Has Much Ground to Make Up on Artificial Intelligence

China’s efforts to become a tech and AI leader come at a time when it is far behind the United States in these critical areas. The number of top researchers in AI in China are ⅕ of that in the US, according to the Center for Data Innovation based in Washington. China also has a shortage of at least 5 million AI workers, according to a 2017 article in the Chinese newspaper People’s Daily.

However, these shortcomings have not prevented the country from setting aggressive targets for its tech workforce. China plans to catch up to the United States in the next year, based on its government blueprint “A Next Generation Artificial Intelligence Development Plan.”

To close the AI and tech talent gap, China is now increasing AI education for children, in addition to efforts to boost the number of coding and AI students from universities. By 2018, there were more than 90 universities in China with AI-related degree programs, up from only 19 the year before.

New Chinese AI Companies Being Launched

There have been many Chinese AI companies that have opened their doors in the past few years, such as SenseTime, iFlytek, Cloudwalk and DJI. These companies have gotten the world’s attention for their abilities in sound recognition, facial recognition, and drone technology. China’s largest technology companies, including Tencent, Alibaba, Huawei, and Baidu, also have put millions of dollars into AI research and development.

Some of these companies have been hurt financially by China’s trade war with the United States, with Washington DC blocking some Chinese technology firms from acquiring highly advanced technologies. But tech experts in China maintain that the roadblocks are just increasing China’s desire to make progress in AI.

Some say that the increasingly fierce trade and tech competition between the two countries is putting pressure on China to boost its capacity for technological innovation. This means the country needs to encourage students to study technology and to become more innovative.

AI Education in Schools Being Ramped Up

In 2018, the Chinese education ministry added artificial intelligence to its high school curriculum. This move has encouraged more than 25 million teenagers to study AI. Also in 2018, China released its first AI textbook for high school students. The textbook introduces basic concepts about image recognition, sound recognition, deep learning, and text recognition. It was put to use in 40 pilot schools last year.

Students who have been exposed to the new curriculum have said they want to read more books that explain the science behind AI, aerospace, big data, and programming. Many also want to join science competitions to learn more about technology.

Other Countries Upping Their AI Game

China is not the only country that is increasing artificial intelligence education. While the private sector is leading the response to AI, governments in France, South Korea, and the US also have established strategies to boost its workforce in the AI sector with more investments. But most of these efforts are at the university level, according to a UNESCO report from 2019. (Unesco.org)

Many EU member states also are reviewing their education curricula to include more lessons about AI and computational thinking in classrooms. Some countries, such as Poland, Austria, and Lithuania, have provided strong computer science education in high schools for years.

The enthusiasm for artificial intelligence education is affecting more than just public policy. The market value of the coding industry for children was only $57 million in 2018 and will surge to more than $4 billion by 2023. That is an increase of 650% in just five years, according to a report by the Chinese consulting company iResearch.

This investment in AI is causing a transformation in classrooms in China. In Shenzhen, a China tech hub, an artificial intelligence program for students in grades 3-8 was piloted in 2019.

Many teachers in China believe that teaching artificial intelligence has other benefits; it helps children to understand scientific concepts when they are young, and also improves their problem-solving abilities, which will enhance their future development.

Welcome to the Roaring 20s – The AI Decade

Many people believe that significant innovations in technology are often taken for granted each day until they are so ingrained in our existence that we must have them. Some examples today are GPS navigation and smartphones with cameras. The amazing complexity of what goes on inside these devices makes them seem simple. (Greenbiz.com)

Perhaps that is why so many of us are fascinated by how artificial intelligence and sustainability intersect. Applications are being made possible every day by breakthroughs in image recognition, machine learning, analytics, and sensors.

In many cases, the combination of these new technologies may transform systems we know well, as well as approaches commonly used in the sustainability and environmental communities. This could make them much smarter with less human intervention.

Example – Camera Trap

A good example is the camera trap, which is a routine method used to study wildlife habits and biodiversity, and one that has been supported by many large technology companies. (Greenbiz.com). That is where Wildlife Insights comes into the picture – a collaboration between Google Earth and seven other companies, one of which is Conservation International.

Wildlife Insights is the biggest database of public camera trap images on Earth. It has 4.5 million images that have been mapped and analyzed with AI for such elements as the year, species, country, and more. Scientists can use the system to upload trap photos, visualize regions, and collect insights about the health of various species.

Here’s the amazing thing: This databased driven by artificial intelligence can analyze more than 3 million pictures per hour, compared to 300 or 1,000 a person can manage. Depending on the species, the accuracy ranges between 80% and 98%. What’s more, the system will discount shots where there is no animal present.

The overall effect of Wildlife Insights is faster insights and analysis. This is important as habitats and species are disappearing. The sustainability community needs to move faster and take action quicker. Some believe the time is running out to act on climate change, and 75% of business decision-makers think AI will be vital to devise solutions that enhance environmental sustainability.

We Should Be Cautious About Possible Side Effects of AI

While the future is bright with AI and its role in sustainability, it also is wise to be cautious. Many believe that AI will be key in building trust and making sure data is governed in ways that are reliable and secure. Also, before people get too excited about what AI can do, it is important that it does not make problems worse. This means spending more time looking at ways to make the data centers that drive AI applications use less energy and be less impactful as far as materials used.

Ethically, some experts have two major concerns here. First is that enough energy is used to ensure data behind AI predictions that we rely on is not biased or flawed. This means it will be necessary to spend more time ensuring that many human perspectives are considered and that the numbers are correct from the beginning. Second, it is critical to view these systems as part of the solution, and not a full replacement for human workers.

Sriram Raghavan, IBM’s VP of AI research, has said that new research from MIT and IBM shows that AI will help with scheduling tasks, but it will not have as much of an impact on jobs that need skills such as industrial strategy and design expertise. Workers in 2020 will likely see these effects as artificial intelligence becomes more routine in the workplace around the globe.

Spending for AI Will Skyrocket by 2023

According to projections by the tech market research firm IDC, spending on AI systems may reach $97 billion in 2023, which is 2.5 times more than the $37.5 billion spent in 2019. (IDC.com). This seems to be happening now because of faster chips, better cameras, larger cloud data-processing services, and other innovations.

Five Areas Where AI Will Make a Difference for Sustainability

Where will applications with AI enabled make a difference for corporate and environmental sustainability? Below are five areas where AI should have a major impact over the next 10 years:

  • Automated energy management: The system for tracking sustainability data today is an old technology that has been with us on PCs for 40 years. Two early examples of how AI can help with energy management have been made by Google, which uses it to enhance efficiency and the renewables it uses in its many data centers. Another is the cold storage firm Lineage Logistics, which uses AI to make schedules for its dozens of warehouses.
  • Enhancing soil conditions and crop yields: Sensors and drones that monitor fields are viewed as a vital part of helping agricultural companies make better decisions about plant nutrition, fighting disease, and hydration. A recent example is a crop emergence solution that was tested by the AI startup Taranis (with John Deere backing), along with the drone spraying company Rantizo. The intelligence driving the system came from Taranis; it uses AI to analyze and monitor aerial imagery.
  • Modeling climate risks in the future: A good example is what is being done with AT&T’s work with Argonne National Laboratory. It is using its proprietary database of information about the telecommunications network it has with Argonne climate models to predict how the effects of climate change could affect business operations up to 30 years in the future. It is expected that insurance companies and large financial services firms to invest in improving their analytics capabilities with artificial intelligence.
  • Protecting biodiversity: The Wildlife Insights initiative is one example of how experts are using imager and data-analysis technology to get a better idea of how the Earth is changing. Google is powering many of these projects, as is Microsoft, which has its AI for Earth project. One effort it funded at the end of 2019 is Wildbook, an effort between the Wild Me Oregon nonprofit and researchers at the University of Chicago, Princeton, and Rensselaer Polytechnic.
  • Checking provenance across supply chains: Many next-gen traceability systems are being tested in different industries from coffee to seafood, and it all comes down to the blockchain. This is a fancy name for electronic ledger technologies. The bottom line here is a lot of machine learning is behind many of these sophisticated applications.

10 Artificial Trends to Watch in 2020

Many people and businesses are getting involved in artificial intelligence (AI), but how many are doing it well? Statistics indicate that the many benefits of AI have yet to be realized in many organizations.

Nine out of 10 companies may have made investments in AI, but 70% report they have seen little impact to date, according to a 2019 MIT report on artificial intelligence use in companies across the globe. (Enterpriseproject.com).

The report stated that CIOs need to be more effective in assessing the value of their AI assets and prove the ROI is worth the investment. Some experts, such as Kara Longo Korte, director of product management at TetraVX, says that 2020 will be the year when organizations focus more on the value AI brings to their business. They will get out of experimentation mode and accelerate AI adoption.

Some of the unfolding AI trends that wise IT leaders may want to follow include:

IT Leaders Will Focus on Measuring the Impact of AI Initiatives

Fewer than 20% of companies have reported business gains from AI in the last 36 months, according to the MIT study mentioned above. This must change in 2020 because of the large investments companies have made in AI.

A way to make this happen is to change how results with AI are measured. Perhaps focus on reporting about things such as ease of use, customer satisfaction and enhanced processes. Experts say CIOs will need to put more money into understanding how AI can help their companies and bring in solutions that offer solid ROI.

Operationalization Is Vital

AI can become the new operating system for the organization. Over the last 10 years, companies have been learning about AI and began working with it. But putting AI models into actual production has been difficult. 2020 could be a tipping point for designing and implementing the infrastructure needed to support AI deployments, offering integrated learning environments and data environments that support AI decision making.

Data Governance Will Become More Pronounced

2020 will feature AI coming into production, but it also will require IT departments to work with the chief data officer and her organization. The problem is sourcing data from many applications and convincing gatekeepers of data to play ball.

Organizations understand the need for the best quality data as the foundation for AI, so in 2020, there may be a better appreciation and need for solid data governance, data engineers, data analysts, and machine learning engineers.

The goal will be to devise a data pipeline that can handle continuous curation to create more successful AI projects. That is why chief data officers are more likely to utilize AI and deep learning for insights initiatives than companies without CDOs.

AI Professionals Will Stand Out

One of the top jobs in Linkedin’s Top 15 Emerging Jobs in the United States for 2020 is AI specialist. Hiring good artificial intelligence professionals has grown almost 75% annually over the last 48 months, Linkedin says. AI and machine learning are becoming synonymous with innovation. Data at Linkedin shows that it is more than just industry buzz. Hot markets for AI professionals are San Francisco, New York, Boston, and Los Angeles.

Data Modeling Will Become More Common

We should expect a shift from cloud-only to cloud-edge hybrid methods to allow machine learning (ML) in 2020. Being able to do high-fidelity, high-resolution, raw machine data analysis in the cloud is expensive and cannot happen in real-time because of ecosystem and transport considerations, according to Senthil Kumar, the VP of software engineering at FogHorn. As of today, companies often settle for small sample sizes or time-deferred data for their projects.

Forrester estimates that edge cloud services will grow by 50% in 2020. If edge-first solutions are implemented, organizations will be able to synthesize their data locally and deliver better predictive capabilities.

AI Will Be Important for B2B

B2B sales and services’ complexity will benefit even more from AI than consumers. Deep and machine learning make it possible for B2B users to match and define complex requirements to trading partners through an intuitive process and a good understanding of trading partner strengths and abilities, according to Keith Hausmann, chief revenue officer at Globality. User experience will get better as AI understands individual preferences better, as well as company requirements with every interaction.

Man and Machine Will Work Together at Contact Centers

Consumer desire to get better service on various digital channels challenges contact center teams. Team leaders need to deal with long wait times, difficult customer journeys, and overwhelmed telephone agents. AI can help agents, allowing them to provide better, more timely responses.

AI in call centers comes with its own challenges, however. It is vital that companies keep their customer service as human-oriented as possible to ensure that the customer journey does not look too automated.

Some brands are using chatbots to reduce customer service costs, but projects that are too ambitious may not resolve customer issues.

Automation Could Increase Significantly

Hyperautomation could be the word for 2020. This means the increased application of AI and ML to fully automate processes and help human beings do their jobs across many tools and at a level of better sophistication. In fact, Gartner has said hyperautomation will be a top strategic technology trend in 2020. (Gartner.com)

Heterogenous Architectures Will Appear

Applications and networks with AI today rely on a variety of different process architectures. This will probably change in 2020. The new generation of AI and ML architectures will feature multimodality and could require more computing resources for their operations. Some top chip manufacturers may move away from their proprietary software stacks and use open Software Development Kits.

Mistakes Will Be Made With AI

AI is far from perfect, and it can increase bias and discrimination. There could be some high-profile public relations disasters, which can damage some companies. Overall, though, trust in AI will not be damaged in the long term.

While AI can encourage discrimination, misuse of facial recognition and overuse of personalization can freak out some customers, these mistakes will highlight how important appropriate AI development and deployment are in the long term.