Daily Archives: February 15, 2020

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In the not-so-far future the polar ice caps have melted and the resulting rise of the ocean waters has drowned all the coastal cities of the world. Withdrawn to the interior of the continents, the human race keeps advancing, reaching the point of creating realistic robots (called mechas) to serve them. One of the mecha-producing companies builds David, an artificial kid which is the first to have real feelings, especially a never-ending love for his "mother", Monica. Monica is the woman who adopted him as a substitute for her real son, who remains in cryo-stasis, stricken by an incurable disease. David is living happily with Monica and her husband, but when their real son returns home after a cure is discovered, his life changes dramatically. Written byChris Makrozahopoulos

Budget:$100,000,000 (estimated)

Opening Weekend USA: $29,352,630,1 July 2001

Gross USA: $78,616,689

Cumulative Worldwide Gross: $235,926,552

Runtime: 146 min

Aspect Ratio: 1.85 : 1

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A.I. Artificial Intelligence (2001) - IMDb

Artificial intelligence (AI), otherwise known as machine learning, is slowly reshaping retail from optimizing back-end supply chain operations to in-store execution. It is also impacting marketing, customer service engagement and anti-fraud activities, according to a report from New York-based information technology industry analyst firm 451 Research.

While AI is far from the mainstream, researchers said plenty of retailers are experimenting with how machine learning can be applied in many areas of retail. The report states retailers wont be the only ones needing to adapt to the disruption of machine learning as customers will also face changes in how they view and experience shopping.

For AI to work to its full potential, researchers said customers will need to be comfortable with increased data sharing if they want to benefit from personalized shopping experiences via machine learning. There will also be those who will struggle with weighing out the benefits of convenience for potentially increased privacy risks.

A recent study by KPMG reviewed the state of AI deployment across retail and other industries. The Capgemini Research Institute estimates AI could add as much as $300 billion in value for the retail sector. As of late 2018, 28% of retailers surveyed by Capgemini were testing AI, up from just 4% in 2016. Capgemini also found AI was creating more jobs than it was replacing.

The majority of use cases focus on customer relations and sales, but Capgemini said there is also a $144 billion savings opportunity from the supply chain through improved efficiency in routing, warehousing, returns management and procurement.

Walmart is using machine learning to automate price markdowns. All clearance markdowns are now automated at the retail giant. The goal is for each store to sell through its product just before the new inventory arrives. In the test stores where machine learning has taken over the inventory management, Walmart said it has increased the sale-through rate by 14% in the first couple of months.

Walmart also recently showcased its Alphabot robotic system in Salem, N.H., by using autonomous carts to retrieve products. Robots assemble orders, then send them to a human employee to check the accuracy, bag them and complete the delivery. Alphabot manages all shelf-stable, refrigerated and frozen products, but fresh products continue to be selected and picked by human employees, the retailer said. Walmart has been testing the Alphabot system for nearly a year, saying the benefits include increased picking speeds of 1,700 picks per hour and storing orders for several hours at appropriate temperatures.

Tom Ward, senior vice president of digital operations at Walmart U.S., said the standard online grocery orders are picked by personal shoppers who fill eight orders at one time, but that is only a fraction of the efficiency achieved with the Alphabot system. Walmart has planned two new Alphabot-enabled warehouses that will serve several store pickup locations. The warehouses will be smaller than the test location in Salem. Given the expense of intuitive technology systems, Walmart officials said it will use them where they make the most sense.

Walmart is also using Bossa Nova robots to scan inventory, a test that was recently expanded to 800 stores in addition to another robotic system being used to scrub floors in hundreds of stores. Machine learning is being used to track inventory, and customer interfaces with chatbots (personal shopping assistants) are being used via the retail giants mobile app.

The National Retail Federation (NRF) recently held its annual conference in New York, and some of the biggest topics discussed were the impact of human-robot interactions and how retailers of all sizes are taking advantage of AI and machine learning across the businesses. Several retailers highlighted ways they were using AI and machine learning across their businesses.

Belk Inc., a department store with nearly 300 stores across 16 states, said it is using AI to help master inventory management. Belk executives said the company is integrating machine learning into ordering, replenishment and allocation systems, including calculating demand for specific sizes by store. Belk said virtual assistants do the heavy lifting, but they are not replacing humans.

Dicks Sporting Goods is also using machine learning to identify patterns and make estimated delivery dates more accurate, according to David Lanners, the companys vice president of retail technology.

Starbucks is also using AI in a process it calls Deep Brew, which leverages AI and machine learning to more accurately manage inventory and ensure adequate staffing for busy periods. The company reports as employees have more time to connect with customers, which has delivered a higher average ticket.

Davids Bridal is also betting on AI to help power its new concierge service designed to help drive more customers into stores. The specialty retailer emerged from bankruptcy in January 2019 and has been working to improve the in-store experience and elevate online customer engagement.

Davids recently launched an AI-powered concierge bot through Apple Business Chat. It connects brands and human customer service agents via bots. Customers use the chatbot to ask questions or seek insights that are shared with stylists. Customers book their appointments online and their questions and online conversation are relayed to the stylist who will assist them in-store.

MKM Partners executive Roxanne Meyer recently said AI may finally be nearing a tipping point as many retailers are exploring the possibilities, but only a few are leveraging it in a meaningful way.

Editors note:The Supply Side sectionof Talk Business & Politics focuses on the companies, organizations, issues and individuals engaged in providing products and services to retailers. The Supply Side is managed by Talk Business & Politics and sponsored byPropak Logistics.

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The Supply Side: Artificial intelligence is slowly shaping the future of retail - talkbusiness.net

Reject it or embrace it. Either way, artificial intelligence is here to stay.

Nobody can predict the future with absolute precision.

But when it comes to the impact of artificial intelligence (AI) on peoples careers, the recent past provides some intriguing clues.

Rhonda Scharfs bookAlexa Is Stealing Your Job: The Impact of Artificial Intelligence on Your Futureoffers some insights and predictions that are well worth our consideration.

In the first two parts of my conversation with Rhonda (see What Role Will [Doe]) Artificial Intelligence Play In Your Life? and Artificial Intelligence, Privacy, And The Choices You Must Make) we discussed the growth of AI in recent years and talked about the privacy concerns of many AI users.

In this final part, we look at how AI is affectingand will continue to affectpeoples career opportunities.

Spoiler alert: theres some good news here.

Rodger Dean Duncan:You quote one researcher who says robots are not here to take away our jobs, theyre here to give us a promotion. What does that mean?

Rhonda Scharf:Much like the computer revolution, we need jobs to maintain the systems that have been created. This creates new, desirable jobs where humans work alongside technology. These new jobs are called the trainers, explainers, and sustainers.

Trainers will teach a machine what it needs to do. For instance, we need to teach a machine that when I yell at it (loud voice), I may be frustrated. It needs to be taught that when I ask it to call Robert, who Robert is and what phone number should be used. Once the machine has a basic understanding, it continues to self-learn, but it needs the basics taught to it (like children do.)

Rhonda Scharf

Explainers are human experts who explain computer behavior to others. They would explain, for example, why a self-driving car performed in a certain way. Or why AI sold shares in a stock at a certain point of the day. The same way lawyers can explain why someone acted in self-defense, when initially his or her actions seemed inappropriate, we need explainers to tell us why a machine did what it did.

Sustainers ensure that our systems are functioning correctly, safely, and responsibly. In the future, theyll ensure that AI systems uphold ethical standards and that industrial robots dont harm humansbecause robots dont understand that were fragile, unlike machinery.

There are going to be many jobs that AI cant replace. We need to think, evolve, interpret, and relate. As smart as a chatbot can be, it will never have the same qualities as my best friend. We will need people for the intangible side of relationships.

Duncan:What should people look for to maximize their careers through the use of AI?

Scharf:According to the World Economic Forum, the top 10 in-demand skills for 2020 include complex problem-solving, critical thinking, creativity, emotional intelligence, judgment and decision-making, and cognitive flexibility. These are the skills that will provide value to your organization. By demonstrating all of these skills, you will be positioning yourself as a valuable resource. Well have AI to handle basic tasks and administrative work. People need complex thinking to propel organizations forward.

Duncan:Bonus: What question do you wish I had asked, and how would you respond?

If you don't want to be left behind, you'd better get educated on AI.

Scharf:I wished you had asked how I felt about artificial intelligence. If I was afraid for my future, for the future of my children, and my childrens children?

The answer is no. I dont think that AI is all the doom and gloom that has been publicized. I also dont believe were about to lead a life of leisure and have the world operate on its own either.

As history has shown us, these types of life-altering changes happen periodically. This is the next one. I believe the way we work is about to change, the same way it changed during the Industrial Revolution, the same way it evolved in response to automation. The way we live is about to change. (Think pasteurization and food storage.) Those who adapt will have a better life for it, and those who refuse to adapt will suffer.

Im confident that I will still be employed for as long as I want to be. My children have only known a life with computers and are open to change, and my future grandchildren will only know a life with AI.

Im excited about our future. Im excited about what AI can bring to my life. I embrace Alexa and all her friends and welcome them into my home.

Link:

How Will Your Career Be Impacted By Artificial Intelligence? - Forbes

Microsoft co-founder Bill Gates makes a point during a Q&A with Margaret Hamburg, board chair for the American Association for the Advancement of Science. (GeekWire Photo / Alan Boyle)

Microsoft co-founder Bill Gates has been working to improve the state of global health through his nonprofit foundation for 20 years, and today he told the nations premier scientific gathering that advances in artificial intelligence and gene editing could accelerate those improvements exponentially in the years ahead.

We have an opportunity with the advance of tools like artificial intelligence and gene-based editing technologies to build this new generation of health solutions so that they are available to everyone on the planet. And Im very excited about this, Gates said in Seattle during a keynote address at the annual meeting of the American Association for the Advancement of Science.

Such tools promise to have a dramatic impact on several of the biggest challenges on the agenda for the Bill & Melinda Gates Foundation, created by the tech guru and his wife in 2000.

When it comes to fighting malaria and other mosquito-borne diseases, for example, CRISPR-Cas9 and other gene-editing tools are being used to change the insects genome to ensure that they cant pass along the parasites that cause those diseases. The Gates Foundation is investing tens of millions of dollars in technologies to spread those genomic changes rapidly through mosquito populations.

Millions more are being spent to find new ways fighting sickle-cell disease and HIV in humans. Gates said techniques now in development could leapfrog beyond the current state of the art for immunological treatments, which require the costly extraction of cells for genetic engineering, followed by the re-infusion of those modified cells in hopes that theyll take hold.

For sickle-cell disease, the vision is to have in-vivo gene editing techniques, that you just do a single injection using vectors that target and edit these blood-forming cells which are down in the bone marrow, with very high efficiency and very few off-target edits, Gates said. A similar in-vivo therapy could provide a functional cure for HIV patients, he said..

The rapid rise of artificial intelligence gives Gates further cause for hope. He noted that that the computational power available for AI applications has been doubling every three and a half months on average, dramatically improving on the two-year doubling rate for chip density thats described by Moores Law.

One project is using AI to look for links between maternal nutrition and infant birth weight. Other projects focus on measuring the balance of different types of microbes in the human gut, using high-throughput gene sequencing. The gut microbiome is thought to play a role in health issues ranging from digestive problems to autoimmune diseases to neurological conditions.

This is an area that needed these sequencing tools and the high-scale data processing, including AI, to be able to find the patterns, Gates said. Theres just too much going on there if you had to do it, say, with paper and pencil to understand the 100 trillion organisms and the large amount of genetic material there. This is a fantastic application for the latest AI technology.

Similarly, organs on a chip could accelerate the pace of biomedical research without putting human experimental subjects at risk.

In simple terms, the technology allows in-vitro modeling of human organs in a way that mimics how they work in the human body, Gates said. Theres some degree of simplification. Most of these systems are single-organ systems. They dont reproduce everything, but some of the key elements we do see there, including some of the disease states for example, with the intestine, the liver, the kidney. It lets us understand drug kinetics and drug activity.

The Gates Foundation has backed a number of organ-on-a-chip projects over the years, including one experiment thats using lymph-node organoids to evaluate the safety and efficacy of vaccines. At least one organ-on-a-chip venture based in the Seattle area, Nortis, has gone commercial thanks in part to Gates support.

High-tech health research tends to come at a high cost, but Gates argues that these technologies will eventually drive down the cost of biomedical innovation.

He also argues that funding from governments and nonprofits will have to play a role in the worlds poorer countries, where those who need advanced medical technologies essentially have no voice in the marketplace.

If the solution of the rich country doesnt scale down then theres this awful thing where it might never happen, Gates said during a Q&A with Margaret Hamburg, who chairs the AAAS board of directors.

But if the acceleration of medical technologies does manage to happen around the world, Gates insists that could have repercussions on the worlds other great challenges, including the growing inequality between rich and poor.

Disease is not only a symptom of inequality, he said, but its a huge cause.

Other tidbits from Gates talk:

Read Gates prepared remarks in a posting to his Gates Notes blog, or watch the video on AAAS YouTube channel.

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Why Bill Gates thinks gene editing and artificial intelligence could save the world - GeekWire

February 14, 2020 -Artificial Intelligence (AI) adoption is gradually becoming more prominent in health systems, but 75 percent of healthcare insiders are concerned that AI could threaten the security and privacy of patient data, according to a recent survey from KPMG.

Although 91 percent of healthcare respondents believe that AI implementation is increasing patient access to care, the survey of 751 US business decision makers uncovered. The survey explored the barriers and challenges that have the potential to hamper the integration of AI technologies in healthcare organizations.

Healthcare security is a top concern for insiders with 75 percent responding that they believe AI could threaten patient data privacy. But 86 percent of respondents said their organizations are taking steps to protect patient privacy as it implements AI.

Organizations believe that a broad understanding of AI and talent in the space are musts to ensure success, but many insiders reported major challenges in these areas.

Despite this, only 47 percent of healthcare insiders responded that their organizations offer AI training courses to employees. While only 67 percent said their employees support AI adoption, the lowest ranking of any industry.

Comprehending the full range of AI technology, and how best to apply it in a healthcare setting, is a learned skill that grows out of pilots and tests. Building an AI-ready workforce requires a wholesale change in the approach to training and how to acquire talent. Having people who understand how AI can solve big, complex problems is critical, Melissa Edwards, managing director and digital enablement at KPMG said in the survey.

Cost is a major barrier for organizations as well. Successful AI implementation requires a large investment, which means that organizations who are already feeling budget-burned may be slower to fund AI.

Thirty-seven percent of healthcare industry executives reported that the pace in which they are implementing AI is too slow.

But Edwards highlighted that the pace has actually greatly increased in the past few years.

The pace with which hospital systems have adopted AI and automation programs has dramatically increased since 2017, she said. Virtually, all major healthcare providers are moving ahead with pilots or programs in these areas. The medical literature is showing support of AIs power as a tool to help clinicians.

Fifty-four percent of executives voiced that to date, AI has increased the overall cost of healthcare. The question is, Where do I put my AI efforts to get the greatest gain for the business? Trying to assess what ROI will look like is a very relevant point as they embark on their AI journey, Edward said.

Last year, The White House called for more transparency and explainability in healthcare AI through the National Artificial Intelligence Research and Development Strategic Plan: 2019 Update.

The plan identified eight strategic priorities for federally-funded AI research including to prioritize investments in the next generation of AI that will drive discovery and insight and enable the US to remain a leader in AI and develop effective methods for human-AI collaboration.

The plan also included:

AI technologies are critical for addressing a range of long-term challenges, such as constructing advanced healthcare systems, a robust intelligent transportation system, and resilient energy and telecommunication networks, the plan concluded.

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Challenges of Artificial Intelligence Adoption in Healthcare - HITInfrastructure.com

Artificial intelligence is reshaping how we live, learn, and work, and this past fall, MIT undergraduates got to explore and build on some of the tools and coming out of research labs at MIT. Through theUndergraduate Research Opportunities Program(UROP), students worked with researchers at the MIT Quest for Intelligence and elsewhere on projects to improve AI literacy and K-12 education, understand face recognition and how the brain forms new memories, and speed up tedious tasks like cataloging new library material. Six projects are featured below.

Programming Jibo to forge an emotional bond with kids

Nicole Thumma met her first robot when she was 5, at a museum.It was incredible that I could have a conversation, even a simple conversation, with this machine, she says. It made me thinkrobotsarethe most complicated manmade thing, which made me want to learn more about them.

Now a senior at MIT, Thumma spent last fall writing dialogue for the social robot Jibo, the brainchild ofMIT Media Lab Associate ProfessorCynthia Breazeal. In a UROP project co-advised by Breazeal and researcherHae Won Park, Thumma scripted mood-appropriate dialogue to help Jibo bond with students while playing learning exercises together.

Because emotions are complicated, Thumma riffed on a set of basic feelings in her dialogue happy/sad, energized/tired, curious/bored. If Jibo was feeling sad, but energetic and curious, she might program it to say, I'm feeling blue today, but something that always cheers me up is talking with my friends, so I'm glad I'm playing with you. A tired, sad, and bored Jibo might say, with a tilt of its head, I don't feel very good. It's like my wires are all mixed up today. I think this activity will help me feel better.

In these brief interactions, Jibo models its vulnerable side and teaches kids how to express their emotions. At the end of an interaction, kids can give Jibo a virtual token to pick up its mood or energy level. They can see what impact they have on others, says Thumma. In all, she wrote 80 lines of dialogue, an experience that led to her to stay on at MIT for an MEng in robotics. The Jibos she helped build are now in kindergarten classrooms in Georgia, offering emotional and intellectual support as they read stories and play word games with their human companions.

Understanding why familiar faces stand out

With a quick glance, the faces of friends and acquaintances jump out from those of strangers. How does the brain do it?Nancy Kanwishers lab in theDepartment of Brain and Cognitive Sciences (BCS) is building computational models to understand the face-recognition process.Two key findings: the brain starts to register the gender and age of a face before recognizing its identity, and that face perception is more robust for familiar faces.

This fall, second-year student Joanne Yuan worked with postdocKatharina Dobsto understandwhy this is so.In earlier experiments, subjects were shown multiple photographs of familiar faces of American celebrities and unfamiliar faces of German celebrities while their brain activity was measured with magnetoencephalography. Dobs found that subjects processed age and gender before the celebrities identity regardless of whether the face was familiar. But they were much better at unpacking the gender and identity of faces they knew, like Scarlett Johansson, for example. Dobs suggests that the improved gender and identity recognition for familiar faces is due to a feed-forward mechanism rather than top-down retrieval of information from memory.

Yuan has explored both hypotheses with a type of model, convolutional neural networks (CNNs), now widely used in face-recognition tools. She trained a CNN on the face images and studied its layers to understand its processing steps. She found that the model, like Dobs human subjects, appeared to process gender and age before identity, suggesting that both CNNs and the brain are primed for face recognition in similar ways. In another experiment, Yuan trained two CNNs on familiar and unfamiliar faces and found that the CNNs, again like humans, were better at identifying the familiar faces.

Yuan says she enjoyed exploring two fields machine learning and neuroscience while gaining an appreciation for the simple act of recognizing faces. Its pretty complicated and theres so much more to learn, she says.

Exploring memory formation

Protruding from the branching dendrites of brain cells are microscopic nubs that grow and change shape as memories form. Improved imaging techniques have allowed researchers to move closer to these nubs, or spines, deep in the brain to learn more about their role in creating and consolidating memories.

Susumu Tonegawa, the Picower Professor of Biology and Neuroscience, haspioneered a technique for labeling clusters of brain cells, called engram cells, that are linked to specific memories in mice. Through conditioning, researchers train a mouse, for example, to recognize an environment. By tracking the evolution of dendritic spines in cells linked to a single memory trace, before and after the learning episode, researchers can estimate where memories may be physically stored.

But it takes time. Hand-labeling spines in a stack of 100 images can take hours more, if the researcher needs to consult images from previous days to verify that a spine-like nub really is one, saysTimothy OConnor, a software engineer in BCS helping with the project.With 400 images taken in a typical session, annotating the images can take longer than collecting them, he adds.

OConnorcontacted the QuestBridgeto see if the process could be automated. Last fall, undergraduates Julian Viera and Peter Hart began work with Bridge AI engineer Katherine Gallagher to train a neural network to automatically pick out the spines. Because spines vary widely in shape and size, teaching the computer what to look for is one big challenge facing the team as the work continues. If successful, the tool could be useful to a hundred other labs across the country.

Its exciting to work on a project that could have a huge amount of impact, says Viera. Its also cool to be learning something new in computer science and neuroscience.

Speeding up the archival process

Each year, Distinctive Collections at the MIT Libraries receivesa large volume of personal letters, lecture notes, and other materials from donors inside and outside of MITthat tell MITs story and document the history of science and technology.Each of these unique items must be organized and described, with a typical box of material taking up to 20 hours to process and make available to users.

To make the work go faster, Andrei Dumitrescu and Efua Akonor, undergraduates at MIT and Wellesley College respectively, are working with Quest Bridges Katherine Gallagher to develop an automated system for processing archival material donated to MIT. Their goal: todevelop a machine-learning pipeline that can categorize and extract information from scanned images of the records. To accomplish this task, they turned to the U.S. Library of Congress (LOC), which has digitized much of its extensive holdings.

This past fall, the students pulled images of about70,000 documents, including correspondence, speeches, lecture notes, photographs, and bookshoused at the LOC, and trained a classifier to distinguish a letter from, say, a speech. They are now using optical character recognition and a text-analysis toolto extract key details likethe date, author, and recipient of a letter, or the date and topic of a lecture. They will soon incorporate object recognition to describe the content of aphotograph,and are looking forward totestingtheir system on the MIT Libraries own digitized data.

Onehighlight of the project was learning to use Google Cloud. This is the real world, where there are no directions, says Dumitrescu. It was fun to figure things out for ourselves.

Inspiring the next generation of robot engineers

From smartphones to smart speakers, a growing number of devices live in the background of our daily lives, hoovering up data. What we lose in privacy we gain in time-saving personalized recommendations and services. Its one of AIs defining tradeoffs that kids should understand, says third-year student PabloAlejo-Aguirre.AI brings usbeautiful andelegant solutions, but it also has its limitations and biases, he says.

Last year, Alejo-Aguirre worked on an AI literacy project co-advised by Cynthia Breazeal and graduate studentRandi Williams. In collaboration with the nonprofiti2 Learning, Breazeals lab has developed an AI curriculum around a robot named Gizmo that teaches kids how totrain their own robotwith an Arduino micro-controller and a user interface based on Scratch-X, a drag-and-drop programming language for children.

To make Gizmo accessible for third-graders, Alejo-Aguirre developed specialized programming blocks that give the robot simple commands like, turn left for one second, or move forward for one second. He added Bluetooth to control Gizmo remotely and simplified its assembly, replacing screws with acrylic plates that slide and click into place. He also gave kids the choice of rabbit and frog-themed Gizmo faces.The new design is a lot sleeker and cleaner, and the edges are more kid-friendly, he says.

After building and testing several prototypes, Alejo-Aguirre and Williams demoed their creation last summer at a robotics camp. This past fall, Alejo-Aguirre manufactured 100 robots that are now in two schools in Boston and a third in western Massachusetts.Im proud of the technical breakthroughs I made through designing, programming, and building the robot, but Im equally proud of the knowledge that will be shared through this curriculum, he says.

Predicting stock prices with machine learning

In search of a practical machine-learning application to learn more about the field, sophomores Dolapo Adedokun and Daniel Adebi hit on stock picking. We all know buy, sell, or hold, says Adedokun. We wanted to find an easy challenge that anyone could relate to, and develop a guide for how to use machine learning in that context.

The two friends approached the Quest Bridge with their own idea for a UROP project after they were turned away by several labs because of their limited programming experience, says Adedokun. Bridge engineer Katherine Gallagher, however, was willing to take on novices. Were building machine-learning tools for non-AI specialists, she says. I was curious to see how Daniel and Dolapo would approach the problem and reason through the questions they encountered.

Adebi wanted to learn more about reinforcement learning, the trial-and-error AI technique that has allowed computers to surpass humans at chess, Go, and a growing list of video games. So, he and Adedokun worked with Gallagher to structure an experiment to see how reinforcement learning would fare against another AI technique, supervised learning, in predicting stock prices.

In reinforcement learning, an agent is turned loose in an unstructured environment with one objective: to maximize a specific outcome (in this case, profits) without being told explicitly how to do so. Supervised learning, by contrast, uses labeled data to accomplish a goal, much like a problem set with the correct answers included.

Adedokun and Adebi trained both models on seven years of stock-price data, from 2010-17, for Amazon, Microsoft, and Google. They then compared profits generated by the reinforcement learning model and a trading algorithm based on the supervised models price predictions for the following 18 months; they found that their reinforcement learning model produced higher returns.

They developed a Jupyter notebook to share what they learned and explain how they built and tested their models. It was a valuable exercise for all of us, says Gallagher. Daniel and Dolapo got hands-on experience with machine-learning fundamentals, and I got insight into the types of obstacles users with their background might face when trying to use the tools were building at the Bridge.

Read more:

Bringing artificial intelligence into the classroom, research lab, and beyond - The MIT Tech

As a psychologist, Tom Gruber is in awe of Facebook. As a computer scientist and citizen of the earth, it scares the crap out of him.

Facebook runs experiments on human behavior that psychologists can only dream about, Gruber says. The trials are done on millions of people, a sample size that's impossible in academia. Dozens of times a day, Mark Zuckerberg tweaks his artificial intelligence to see what will keep his 2.5 billion subscribers scrolling through Facebook, and to make them confuse advertising with news so they click on the ads, Gruber says.

"They have the world's largest psychology experiment at their disposal every single day," Gruber says. "They can do experiments that science can't do, at scale."

Gruber, who speaks at TechfestNW this April, is hardly a bomb-thrower. He is a pioneer in artificial intelligence and the co-inventor of Siri, the digital assistant on the iPhone that uses AI and speech recognition to answer billions of questions each year.

Since selling Siri to Apple in 2010, though, Gruber has become one of a small group of technologists who have grown wary of the AI they helped create. He plans to talk about the dangerand promiseof artificial intelligence at TechfestNW.

Facebook and YouTube have more than 2 billion users each, making them as big as the world's two biggest religions, Christianity and Islam, Gruber says.

"And I would add that even the people who pray to Mecca five times a day, only do it five times a day," Gruber says. "Our millennials check their phones 150 times a day."

Gruber has deep roots in techdom. He earned a bachelor's degree in computer science and psychology from Loyola University in New Orleans, got his Ph.D. in computer and information science from the University of Massachusetts, then did research at Stanford University for five years.

Siri grew out of a Stanford spinoff called SRI International. Gruber consulted at SRI in 2007, and, soon after, he and two others, Dag Kittlaus and Adam Cheyer, spun off newer digital-assistant technology that went beyond the DARPA work. They named the new company Siri, which means "beautiful woman who leads you to victory."

Siri is actually a collection of powerful neural networks: mathematical formulas running on computers that analyze huge amounts of data and learn the patterns within them. Turn a neural net loose on a million samples of spoken language, and it will start to recognize words and their meaning. No longer do programmers have to tell computers what to do, logic step by logic step.

Steve Jobs persuaded Gruber and his partners to sell to Apple in 2010 for some $200 million, according to Wired magazine.

Gruber retired from Apple in 2018 and founded Humanistic AI, a firm that helps companies use machine intelligence to collaborate with humans, not replaceor terrorizethem.

Unlike some AI doomsayers, including Tesla inventor Elon Musk and podcasting neuroscientist Sam Harris, Gruber thinks AI can be tamed. Right now, it's a science experiment gone wrong. Frankenstein never meant for his monster to become a killer, and Zuckerberg, he says, never intended Facebook to set us at each other's throats, over politics or anything else.

"My argument is that this is an unintended consequence," Gruber says. "We'll give them a pass on being evil geniuses. Maybe some of them are. But let's assume good intentions."

When it comes to Zuckerberg, assuming good intentions is controversial. In July, Facebook agreed to pay a record $5 billion fine to settle charges by the Federal Trade Commission that it abused users' personal information.

So call Gruber an optimist. He thinks the same algorithms that prey on our bad habits can be used to encourage good ones.

Tech companies make excuses for why they can't police their networks, and most involve money. So far, humans are better at sorting lies from truth, and hate from news. That means you have to hire a lot of humans, which is anathema to the tech monopolies. Gruber says they need to suck it up.

"It's like when the auto industry said, 'Air bags are going to put us out of business, so don't impose this onerous thing on us,'" Gruber says. "It's all bullshit."

And there's more. Why not run all these vast experiments on human behavior to improve human life, instead of wrecking it? Why not use AI to change the habits that lead to type 2 diabetes, heart disease, hypertension, and suicide?

"We have weak theories about what makes people tick, and what to do to help them do better things," Gruber says. "But AI has shown that if you want to get 2 billion people addicted to something that's not good for them, you can do it."

AI doesn't know if it's operating for good or evil, Gruber says. Someday it may, but for now, it's up to humans to direct it.

So far, we've been crappy shepherds.

GO: TechfestNW is at Portland State University's Viking Pavilion, 930 SW Hall St., techfestnw.com. Thursday-Friday, April 2-3. Visit the website for tickets.

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The Father of Siri Has Grown Wary of the Artificial Intelligence He Helped Create - Willamette Week

When it comes to artificial intelligence (AI), America is still on top. A major reason for that? America is quite good at recruiting AI students from around the world and retaining them after they graduate from school. But if certain plans proposed by the White House come to fruition, then that could all change very quickly.

Americas increasingly convoluted and unwelcoming immigration system has put the countrys front-runner status in jeopardy, according to a landmark report from Georgetown Universitys Center for Security and Technology (CSET). And, if the Trump administration moves forward with its plan to rescind the Optional Practical Training (OPT) program, then Americas. leadership position in AI will become even more vulnerable.

Rescinding OPT, which allows thousands of international students to work in the United States after graduation, notably undermines President Donald Trumps own plan to protect Americas AI advantage. More than two hundred thousand people are brought into the American workforce each year through the OPT program. Roughly two-thirds of them hold the sorely-needed advanced graduate and Ph.D. degrees that AI relies on.

If theyre gone, then Americas workforce has a problem. After all, domestic interest in these fields remains woefully insufficient to meet the spike in employer demand for AI workers.

These kinds of labor shortages cant be taken lightly, given the beneficial impact AI has on American lives. AI is improving a vast and diverse range of industries including healthcare, entertainment, auto manufacturing, social media, finance, and many, many more, Matthew Feeney, the Cato Institutes Director of Emerging Technologies, told me. In fact, the OPT program helps ameliorate talent gaps that may otherwise see that crucial progress grind to a halt.

Right now, OPT offers graduates in science, technology, engineering, and mathematics (STEM) the chance to work in the United States for up to three years after graduation. Theres no limit to the number of people who can participate in the program, and, while working, many of these folks hope to nab an H-1B visa, which can give them an additional six years of U.S. employment. Firms can also sponsor their H-1B workers for green cards, making it one of the few viable ways that foreign students can gain permanent residency.

But the H-1B visa is subject to strict numerical limits. In fact, for every year since 2015, the number of H-1B applications the U.S. government received was more than double that of the eighty-five thousand visas available. The executive branch has dealt with this excess in applications by awarding visas through a lottery system. Since an applicant may not get an H-1B visa on their first try, the OPT program allows them to re-apply for a second and third year while they maintain their authorization to work in the United States.

As such, the OPT program is treated as a waiting room of sorts for immigrant hopefuls. An applicant may not get an H-1B visa on their first try, but OPT allows STEM graduates, including those in AI-related fields, to keep working in the United States as they reapply the next year and, if theyre still unlucky, the year after.

By the end of August, that could all come to a crashing halt. The Trump administration is slated to curtail the programeven possibly dismantling it altogether.

Even if Trump chooses not to do so, the program still faces challenges in court. In Washington Alliance of Technology Workers v. U.S. Department of Homeland Security, et. al, a union of U.S. tech workers have argued that OPT harms Americans by forcing them to compete with foreigners for the same employment opportunities.

Extensive research on the program, however, shows that this simply isnt the case. According to a study by Jeremy Neufeld of the Niskanen Center, for every ten OPT workers in a given locality, American wages increase by an average of at least $2and five patents get added to that area. OPT recipients are highly educated and motivated by the desire to prove themselves, says Neufeld. They even make the people around them and the teams theyre on more productive, increasing innovation and raising the earnings of similarly educated natives.

The OPT program is under fire from both the executive and legislative branches, so its up to Congress to make certain that America continues to be an attractive place for AI graduates to build their futures. For starters, lawmakers could consider codifying OPT into law, so that the programs fate no longer rests on executive discretion. Congress should also examine solutions such as extending the H-1B cap exemptions that many nonprofit and governmental organizations already enjoyand ending green card restrictions that would keep some applicants waiting in line for over one hundred years.

The OPT program has been one of the many Band-Aids holding together an immigration system that hasnt been updated since the early 1990s before the internet became the world economys major driver. The Trump administration is moving to eliminate this crucial but imperfect fix. But Congress can use this opportunity to make lasting changesreforms that recognize Americas desperate need to increase and keep the AI talent Trump said he wanted.

Sam Peak is a Tech and Innovation Fellow for Young Voices and a policy associate at Americans for Prosperity. The views expressed in this column dont necessarily reflect those of the organizations with which the author is affiliated.

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Trump Policy Tactics that Target Foreigners Put America's Artificial Intelligence at Risk - The National Interest Online

Alterovitz is also looking for other uses to help VA staff members make better use of their time and help patients in areas where resources are limited.

"Being able to cut the (clinician) workload down is one way to do that," he said. "Other ways are working on processes, so reducing patient wait times, analyzing paperwork, etc."

Barriers to AI

But Alterovitz notes there are challenges to implementing AI, including privacy concerns and trying to understand how and why AI systems make decisions.

Last year, DeepMind Technologies, an AI firm owned by Google, used VA data to test a system to predict deadly kidney disease. But for every correct prediction, there were two false positives.

Those false results may cause doctors to recommend inappropriate treatments, run unnecessary tests, or do other things that could harm patients, waste time, and reduce confidence in the technology.

"It's important for AI systems to be tested in real-world environments with real-world patients and clinicians, because there can be unintended consequences," said Mildred Cho, the Associate Director of the Stanford Center for Biomedical Ethics.

Cho also said it's important to test AI systems with a variety of demographics, because what may work for one population may not for another. The DeepMind study acknowledged that more than 90 percent of the patients in the dataset it used to test the system were male veterans, and that performance was lower for females.

Alterovitz said the VA is taking those concerns into account as the agency experiments with AI and tries to improve upon the technology to ensure it is reliable and effective.

This story was produced by the American Homefront Project, a public media collaboration that reports on American military life and veterans. Funding comes from the Corporation for Public Broadcasting.

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The VA Has Embraced Artificial Intelligence To Improve Veterans' Health Care - WUNC

Is there room in theworld for two Travis Scotts?

Digital agency Space150 recently created a completely AI-generated song with lyrics and melodies designed with Scotts music in mind, purely for his unique style, Adweek reports. After fueling a text generator model with lyrics for two weeks, the company ended up with a track titled Jack Park Canny Dope Man." It also came with a strange videorapped by a deepfake of Scott named Travisbott, which you can view above.

The song even includesScotts trademark ad-libs, and has extremely Auto-Tuned vocals and absurd rhymes, such as:

I aint got the surfers 'cause I know Im not that hardBut I got all my old bitches mad by the barsThinkin at the Grammys, in the family, I got starsTry to put in the plane, but the blame be on the cars

Space150s executive creative director, Ned Lampert told Adweekthe project wasnt created for any specific client. We were sort of fascinated with like, What if we tried to make a songlike an actual good songby using AI and basically creative directing AI? he said. And so we chose Travis Scott just because he is just such a unique artist and he has a unique sound and everything sort of has an aesthetic to it, both audibly and visually.

Lampert explained that the bot kept producing lyrics about eating, while still figuring out how to imitate Scotts musical style. There was one line like, I dont want to fuck your party food, he said.

It came up with things that we would never come up with, Lampert continued. I love the beautiful mistakes that we make all the time that get turned into work or [situations] where someone says something ridiculous and then we end up doing it. And there were some of those types of behaviors within this process.

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This Travis Scott-Type Song Was Created by Artificial Intelligence - Complex