Introduction

In 2019, over 40 million Americans wagered money on March Madness brackets, according to the American Gaming Association. Most of this money was bet in bracket pools, which consist of a group of people each entering their predictions of the NCAA tournament games along with a buy-in. The bracket that comes closest to being right wins. If you also consider the bracket pools where only pride is at stake, the number of participants is much greater. Despite all this attention, most do not give themselves the best chance to win because they are focused on the wrong question.

The Right Question

Mistake #3 in Dr. John Elders Top 10 Data Science Mistakes is to ask the wrong question. A cornerstone of any successful analytics project starts with having the right project goal; that is, to aim at the right target. If youre like most people, when you fill out your bracket, you ask yourself, What do I think is most likely to happen? This is the wrong question to ask if you are competing in a pool because the objective is to win money, NOT to make the most correct bracket. The correct question to ask is: What bracket gives me the best chance to win $? (This requires studying the payout formula. I used ESPN standard scoring (320 possible points per round) with all pool money given to the winner. (10 points are awarded for each correct win in the round of 64, 20 in the round of 32, and so forth, doubling until 320 are awarded for a correct championship call.))

While these questions seem similar, the brackets they produce will be significantly different.

If you ignore your opponents and pick the teams with the best chance to win games you will reduce your chance of winning money. Even the strongest team is unlikely to win it all, and even if they do, plenty of your opponents likely picked them as well. The best way to optimize your chances of making money is to choose a champion team with a good chance to win who is unpopular with your opponents.

Knowing how other people in your pool are filling out their brackets is crucial, because it helps you identify teams that are less likely to be picked. One way to see how others are filling out their brackets is via ESPNs Who Picked Whom page (Figure 1). It summarizes how often each team is picked to advance in each round across all ESPN brackets and is a great first step towards identifying overlooked teams.

Figure 1. ESPNs Who Picked Whom Tournament Challenge page

For a team to be overlooked, their perceived chance to win must be lower than their actual chance to win. The Who Picked Whom page provides an estimate of perceived chance to win, but to find undervalued teams we also need estimates for actual chance to win. This can range from a complex prediction model to your own gut feeling. Two sources I trust are 538s March Madness predictions and Vegas future betting odds. 538s predictions are based on a combination of computer rankings and has predicted performance well in past tournaments. There is also reason to pay attention to Vegas odds, because if they were too far off, the sportsbooks would lose money.

However, both sources have their flaws. 538 is based on computer ratings, so while they avoid human bias, they miss out on expert intuition. Most Vegas sportsbooks likely use both computer ratings and expert intuition to create their betting odds, but they are strongly motivated to have equal betting on all sides, so they are significantly affected by human perception. For example, if everyone was betting on Duke to win the NCAA tournament, they would increase Dukes betting odds so that more people would bet on other teams to avoid large losses. When calculating win probabilities for this article, I chose to average 538 and Vegas predictions to obtain a balance I was comfortable with.

Lets look at last year. Figure 2 compares a teams perceived chance to win (based on ESPNs Who Picked Whom) to their actual chance to win (based on 538-Vegas averaged predictions) for the leading 2019 NCAA Tournament teams. (Probabilities for all 64 teams in the tournament appear in Table 6 in the Appendix.)

Figure 2. Actual versus perceived chance to win March Madness for 8 top teams

As shown in Figure 2, participants over-picked Duke and North Carolina as champions and under-picked Gonzaga and Virginia. Many factors contributed to these selections; for example, most predictive models, avid sports fans, and bettors agreed that Duke was the best team last year. If you were the picking the bracket most likely to occur, then selecting Duke as champion was the natural pick. But ignoring selections made by others in your pool wont help you win your pool.

While this graph is interesting, how can we turn it into concrete takeaways? Gonzaga and Virginia look like good picks, but what about the rest of the teams hidden in that bottom left corner? Does it ever make sense to pick teams like Texas Tech, who had a 2.6% chance to win it all, and only 0.9% of brackets picking them? How much does picking an overvalued favorite like Duke hurt your chances of winning your pool?

To answer these questions, I simulated many bracket pools and found that the teams in Gonzagas and Virginias spots are usually the best picksthe most undervalued of the top four to five favorites. However, as the size of your bracket pool increases, overlooked lower seeds like third-seeded Texas Tech or fourth-seeded Virginia Tech become more attractive. The logic for this is simple: the chance that one of these teams wins it all is small, but if they do, then you probably win your pool regardless of the number of participants, because its likely no one else picked them.

Simulations Methodology

To simulate bracket pools, I first had to simulate brackets. I used an average of the Vegas and 538 predictions to run many simulations of the actual events of March Madness. As discussed above, this method isnt perfect but its a good approximation. Next, I used the Who Picked Whom page to simulate many human-created brackets. For each human bracket, I calculated the chance it would win a pool of size by first finding its percentile ranking among all human brackets assuming one of the 538-Vegas simulated brackets were the real events. This percentile is basically the chance it is better than a random bracket. I raised the percentile to the power, and then repeated for all simulated 538-Vegas brackets, averaging the results to get a single win probability per bracket.

For example, lets say for one 538-Vegas simulation, my bracket is in the 90th percentile of all human brackets, and there are nine other people in my pool. The chance I win the pool would be. If we assumed a different simulation, then my bracket might only be in the 20th percentile, which would make my win probability . By averaging these probabilities for all 538-Vegas simulations we can calculate an estimate of a brackets win probability in a pool of size , assuming we trust our input sources.

Results

I used this methodology to simulate bracket pools with 10, 20, 50, 100, and 1000 participants. The detailed results of the simulations are shown in Tables 1-6 in the Appendix. Virginia and Gonzaga were the best champion picks when the pool had 50 or fewer participants. Yet, interestingly, Texas Tech and Purdue (3-seeds) and Virginia Tech (4-seed) were as good or better champion picks when the pool had 100 or more participants.

General takeaways from the simulations:

Additional Thoughts

We have assumed that your local pool makes their selections just like the rest of America, which probably isnt true. If you live close to a team thats in the tournament, then that team will likely be over-picked. For example, I live in Charlottesville (home of the University of Virginia), and Virginia has been picked as the champion in roughly 40% of brackets in my pools over the past couple of years. If you live close to a team with a high seed, one strategy is to start with ESPNs Who Picked Whom odds, and then boost the odds of the popular local team and correspondingly drop the odds for all other teams. Another strategy Ive used is to ask people in my pool who they are picking. It is mutually beneficial, since Id be less likely to pick whoever they are picking.

As a parting thought, I want to describe a scenario from the 2019 NCAA tournament some of you may be familiar with. Auburn, a five seed, was winning by two points in the waning moments of the game, when they inexplicably fouled the other team in the act of shooting a three-point shot with one second to go. The opposing player, a 78% free throw shooter, stepped to the line and missed two out of three shots, allowing Auburn to advance. This isnt an alternate reality; this is how Auburn won their first-round game against 12-seeded New Mexico State. They proceeded to beat powerhouses Kansas, North Carolina, and Kentucky on their way to the Final Four, where they faced the exact same situation against Virginia. Virginias Kyle Guy made all his three free throws, and Virginia went on to win the championship.

I add this to highlight an important qualifier of this analysisits impossible to accurately predict March Madness. Were the people who picked Auburn to go to the Final Four geniuses? Of course not. Had Terrell Brown of New Mexico State made his free throws, they would have looked silly. There is no perfect model that can predict the future, and those who do well in the pools are not basketball gurus, they are just lucky. Implementing the strategies talked about here wont guarantee a victory; they just reduce the amount of luck you need to win. And even with the best modelsyoull still need a lot of luck. It is March Madness, after all.

Appendix: Detailed Analyses by Bracket Sizes

At baseline (randomly), a bracket in a ten-person pool has a 10% chance to win. Table 1 shows how that chance changes based on the round selected for a given team to lose. For example, brackets that had Virginia losing in the Round of 64 won a ten-person pool 4.2% of the time, while brackets that picked them to win it all won 15.1% of the time. As a reminder, these simulations were done with only pre-tournament informationthey had no data indicating that Virginia was the eventual champion, of course.

Table 1 Probability that a bracket wins a ten-person bracket pool given that it had a given team (row) making it to a given round (column) and no further

In ten-person pools, the best performing brackets were those that picked Virginia or Gonzaga as the champion, winning 15% of the time. Notably, early round picks did not have a big influence on the chance of winning the pool, the exception being brackets that had a one or two seed losing in the first round. Brackets that had a three seed or lower as champion performed very poorly, but having lower seeds making the Final Four did not have a significant impact on chance of winning.

Table 2 shows the same information for bracket pools with 20 people. The baseline chance is now 5%, and again the best performing brackets are those that picked Virginia or Gonzaga to win. Similarly, picks in the first few rounds do not have much influence. Michigan State has now risen to the third best Champion pick, and interestingly Purdue is the third best runner-up pick.

Table 2 Probability that a bracket wins a 20-person bracket pool given that it had a given team (row) making it to a given round (column) and no further

When the bracket pool size increases to 50, as shown in Table 3, picking the overvalued favorites (Duke and North Carolina) as champions significantly lowers your baseline chances (2%). The slightly undervalued two and three seeds now raise your baseline chances when selected as champions, but Virginia and Gonzaga remain the best picks.

Table 3 Probability that a bracket wins a 50-person bracket pool given that it had a given team (row) making it to a given round (column) and no further

With the bracket pool size at 100 (Table 4), Virginia and Gonzaga are joined by undervalued three-seeds Texas Tech and Purdue. Picking any of these four raises your baseline chances from 1% to close to 2%. Picking Duke or North Carolina again hurts your chances.

Table 4 Probability that a bracket wins a 100-person bracket pool given that it had a given team (row) making it to a given round (column) and no further

When the bracket pool grows to 1000 people (Table 5), there is a complete changing of the guard. Virginia Tech is now the optimal champion pick, raising your baseline chance of winning your pool from 0.1% to 0.4%, followed by the three-seeds and sixth-seeded Iowa State are the best champion picks.

Table 5 Probability that a bracket wins a 1000-person bracket pool given that it had a given team (row) making it to a given round (column) and no further

For Reference, Table 6 shows the actual chance to win versus the chance of being picked to win for all teams seeded seventh or better. These chances are derived from the ESPN Who Picked Whom page and the 538-Vegas predictions. The data for the top eight teams in Table 6 is plotted in Figure 2. Notably, Duke and North Carolina are overvalued, while the rest are all at least slightly undervalued.

The teams in bold in Table 6 are examples of teams that are good champion picks in larger pools. They all have a high ratio of actual chance to win to chance of being picked to win, but a low overall actual chance to win.

Table 6 Actual odds to win Championship vs Chance Team is Picked to Win Championship.

Undervalued teams in green; over-valued in red.

About the Author

Robert Robison is an experienced engineer and data analyst who loves to challenge assumptions and think outside the box. He enjoys learning new skills and techniques to reveal value in data. Robert earned a BS in Aerospace Engineering from the University of Virginia, and is completing an MS in Analytics through Georgia Tech.

In his free time, Robert enjoys playing volleyball and basketball, watching basketball and football, reading, hiking, and doing anything with his wife, Lauren.

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How to Pick a Winning March Madness Bracket - Machine Learning Times - machine learning & data science news - The Predictive Analytics Times

SAN JOSE, Calif., Feb. 21, 2020 Recently, the international evaluation agency Standard Performance Evaluation Corporation (SPEC) has finalized the election of new Open System Steering Committee (OSSC) executive members, which include Inspur, Intel, AMD, IBM, Oracle and other three companies.

It is worth noting that Inspur, a re-elected OSSC member, was also re-elected as the chair of the SPEC Machine Learning (SPEC ML) working group. The development plan of ML test benchmark proposed by Inspur has been approved by members which aims to provide users with standard on evaluating machine learning computing performance.

SPEC is a global and authoritative third-party application performance testing organization established in 1988, which aims to establish and maintain a series of performance, function, and energy consumption benchmarks, and provides important reference standards for users to evaluate the performance and energy efficiency of computing systems. The organization consists of 138 well-known technology companies, universities and research institutions in the industry such as Intel, Oracle, NVIDIA, Apple, Microsoft, Inspur, Berkeley, Lawrence Berkeley National Laboratory, etc., and its test standard has become an important indicator for many users to evaluate overall computing performance.

The OSSC executive committee is the permanent body of the SPEC OSG (short for Open System Group, the earliest and largest committee established by SPEC) and is responsible for supervising and reviewing the daily work of major technical groups of OSG, major issues, additions and deletions of members, development direction of research and decision of testing standards, etc. Meanwhile, OSSC executive committee uniformly manages the development and maintenance of SPEC CPU, SPEC Power, SPEC Java, SPEC Virt and other benchmarks.

Machine Learning is an important direction in AI development. Different computing accelerator technologies such as GPU, FPGA, ASIC, and different AI frameworks such as TensorFlow and Pytorch provide customers with a rich marketplace of options. However, the next important thing for the customer to consider is how to evaluate the computing efficiency of various AI computing platforms. Both enterprises and research institutions require a set of benchmarks and methods to effectively measure performance to find the right solution for their needs.

In the past year, Inspur has done much to advance the SPEC ML standard specific component development, contributing test models, architectures, use cases, methods and so on, which have been duly acknowledged by SPEC organization and its members.

Joe Qiao, General Manager of Inspur Solution and Evaluation Department, believes that SPEC ML can provide an objective comparison standard for AI / ML applications, which will help users choose a computing system that best meet their application needs. Meanwhile, it also provides a unified measurement standard for manufacturers to improve their technologies and solution capabilities, advancing the development of the AI industry.

About Inspur

Inspur is a leading provider of data center infrastructure, cloud computing, and AI solutions, ranking among the worlds top 3 server manufacturers. Through engineering and innovation, Inspur delivers cutting-edge computing hardware design and extensive product offerings to address important technology arenas like open computing, cloud data center, AI and deep learning. Performance-optimized and purpose-built, our world-class solutions empower customers to tackle specific workloads and real-world challenges. To learn more, please go towww.inspursystems.com.

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Inspur Re-Elected as Member of SPEC OSSC and Chair of SPEC Machine Learning - HPCwire

Microsoft has announced a new application, Dynamics 365 Project Operations, as well as additional AI-driven features for its Dynamics 365 range.

If you are averse to buzzwords, look away now. Microsoft Business Applications President James Phillips announced the new features in a post which promises AI-driven insights, a holistic 360-degree view of a customer, personalized customer experiences across every touchpoint, and real-time actionable insights.

Dynamics 365 is Microsofts cloud-based suite of business applications covering sales, marketing, customer service, field service, human resources, finance, supply chain management and more. There are even mixed reality offerings for product visualisation and remote assistance.

Dynamics is a growing business for Microsoft, thanks in part to integration with Office 365, even though some of the applications are quirky and awkward to use in places. Licensing is complex too and can be expensive.

Keeping up with what is new is a challenge. If you have a few hours to spare, you could read the 546-page 2019 Release Wave 2 [PDF] document, for features which have mostly been delivered, or the 405-page 2020 Release Wave 1 [PDF], about what is coming from April to September this year.

Many of the new features are small tweaks, but the company is also putting its energy into connecting data, both from internal business sources and from third parties, to drive AI analytics.

The updated Dynamics 365 Customer Insights includes data sources such as demographics and interests, firmographics, market trends, and product and service usage data, says Phillips. AI is also used in new forecasting features in Dynamics 365 Sales and in Dynamics 365 Finance Insights, coming in preview in May.

Dynamics 365 Project Operations ... Click to enlarge

The company is also introducing a new application, Dynamics 365 Business Operations, with general availability promised for October 1 2020. This looks like a business-oriented take on project management, with the ability to generate quotes, track progress, allocate resources, and generate invoices.

Microsoft already offers project management through its Project products, though this is part of Office rather than Dynamics. What can you do with Project Operations that you could not do before with a combination of Project and Dynamics 365?

There is not a lot of detail in the overview, but rest assured that it has AI-powered business insights and seamless interoperability with Microsoft Teams, so it must be great, right? More will no doubt be revealed at the May Business Applications Summit in Dallas, Texas.

Sponsored: Detecting cyber attacks as a small to medium business

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Buzzwords ahoy as Microsoft tears the wraps off machine-learning enhancements, new application for Dynamics 365 - The Register

Cisco on Friday announced advancements to its IoT portfolio that enable service provider partners to offer optimized management of cellular IoT environments and new 5G use-cases.

Cisco IoT Control Center(formerly Jasper Control Center) is introducing new innovations to improve management and reduce deployment complexity. These include:

Using Machine Learning (ML) to improve management: With visibility into 3 billion events every day, Cisco IoT Control Center uses the industry's broadest visibility to enable machine learning models to quickly identify anomalies and address issues before they impact a customer. Service providers can also identify and alert customers of errant devices, allowing for greater endpoint security and control.

Smart billing to optimize rate plans:Service providers can improve customer satisfaction by enabling Smart billing to automatically optimize rate plans. Policies can also be created to proactively send customer notifications should usage changes or rate plans need to be updated to help save enterprises money.

Support for global supply chains: SIM portability is an enterprise requirement to support complex supply chains spanning multiple service providers and geographies. It is time-consuming and requires integrations between many different service providers and vendors, driving up costs for both. Cisco IoT Control Center now provides eSIM as a service, enabling a true turnkey SIM portability solution to deliver fast, reliable, cost-effective SIM handoffs between service providers.

Cisco IoT Control Center has taken steps towards 5G readiness to incubate and promote high value 5G business use cases that customers can easily adopt.

Vikas Butaney, VP Product Management IoT, Cisco Cellular IoT deployments are accelerating across connected cars, utilities and transportation industries and with 5G and Wi-Fi 6 on the horizon IoT adoption will grow even faster. Cisco is investing in connectivity management, IoT networking, IoT security, and edge computing to accelerate the adoption of IoT use-cases.

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Cisco Enhances IoT Platform with 5G Readiness and Machine Learning - The Fast Mode

Alexa, Im depressed. The idea that a tabletop virtual assistant such as Alexa or Siri knows or cares how youre feeling sounds straight out of some neurotic comedy. Today, at least.

Mental health is a brave new frontier for artificial-intelligence and machine-learning algorithms driven by big data. Before long, if some forward-looking psychologists, doctors and venture-capital investors have their way, your therapist could be a virtual human able to listen, counsel and even bill for that 50-minute hour.

AI will be a game-changer, says James Lake, a California psychiatrist and author of a series of self-help e-books showing individuals how to integrate a broad-based plan for their mental health. AI tools, he adds, will allow mental-health providers to optimize patient care on the basis of each individuals unique history, symptoms, needs, financial constraints and preferences.

What if you need an anti-depressant or another psychiatric medication? AI can help a psychiatrist pinpoint the exact drug or drug class that your body will respond to, shortening or even eliminating the trial and error and its side-effects that frustrates both patients and doctors. Algorithms also can tell, based on someones age, gender, responses to questions and other factors, if that person is about to attempt suicide; Facebook FB, -2.05%, for example, uses an algorithm that flags a post if it contains words that suggest suicidal thoughts or self-harm.

As mental disorders rise the cost to the global economy is projected to be $16 trillion over the next decade, according to the Lancet Commission caring for patients with precision is a Holy Grail for mental-health professionals. Current diagnosis and treatment methods, while skilled and insightful, cannot fully capture the unique needs and complexity of every patient not without time, money and a willingness that many people simply do not have. AI-based therapies have the potential to be faster and cheaper, and therefore more effective, which in turn can encourage patients to continue their counseling.

Data-based precision mental health also appeals to cost-conscious employers and insurance plans. Startups with traction in this area include Quartet Health, whose backers include GV (formerly Google Ventures), a unit of Alphabet GOOG, -2.18% GOOGL, -2.21%, which has partnered with health-care systems and health plans in several U.S. states, with a particular focus on underserved Medicaid patients. Another startup, Lyra Health, matches employees to health professionals using big data to diagnose mental conditions, and counts eBay EBAY, +1.35% and Amgen AMGN, +0.29% among its customers.

We can predict whether someone would recover if they took a specific treatment, says Adam Chekroud, a clinical psychologist and co-founder of Spring Health, a startup whose predictive models detect mental states and recommend appropriate treatment.

Some large companies including Gap GPS, -2.35% and Amazons AMZN, -2.65% Whole Foods use Spring Healths technology for their employees. After answering questions about personal problems and behaviors, employees are directed to an in-network provider who is given specific treatments that Spring Health determines are most likely to help that patient. Adds Chekroud: When people did what was predicted [for them], they were twice as likely to recover.

Encouraging outcomes are also apparent from smartphone-based chatbots that use AI to deliver cognitive behavioral therapy, or CBT, which can help emotionally troubled people build life skills and self-compassion. CBT is a proven way to treat depression, and CBT-based chatbots, or conversational agents which simulate human conversation through voice and text have been shown to reduce depressive episodes in users after just two weeks of daily interaction.

For example, Woebot, a chatbot that Stanford University clinicians originally developed for college students, is now a downloadable app with venture funding. Woebot introduces itself as an emotional assistant that is like a wise little person you can consult with during difficult times, and not-so-difficult times. Its chatty interface is friendly and colloquial, gently probing about feelings and habits. This allows me to find patterns that are sometimes hard for humans to see, Woebot explains.

Chatbots like Woebot aim to tap into the root of psychotherapy a therapeutic relationship of trust, connection, and a patients belief that a provider understands and cares about their feelings, thoughts and experiences. Chatbots arent yet so sophisticated, but Woebot reminds you that it will check in every day, and a session ends with the app offering an element of positive psychology, such as practicing gratitude.

Clearly, AI has the potential to reshape mental-health care in powerful and meaningful ways if people choose to get help, or are able to find it. One of every five adults in the U.S. experienced mental illness in 2018, according to the U.S. Department of Health and Human Services, but less than half received treatment. One reason is that psychiatrists and psychologists in the U.S. are concentrated in urban areas, mostly in the Northeast and on the West Coast. More than three times as many psychologists practice in New England, for example, than in the Gulf states. Many rural counties have no mental-health professionals.

Perhaps the biggest obstacle to treatment is an age-old cultural stigma about mental illness and therapy. The irony is that while mental disorders now are more out in the open, privately many patients and their families carry shame and embarrassment about it. Add financial constraints and lack of health insurance for some, and its clear why many people who need psychological counseling go untreated. The gap in care is global: the World Health Organization reports that one in four people globally will suffer from psychological distress at some point in life, but two-thirds will never seek help.

Stigma is something we can do a great deal about, says Bandy Lee, a psychiatrist on the faculty of the Yale School of Medicines Law and Psychiatry Division. It takes very little to acquire an attitude of openness and educate the public in ways that could change the way we view mental illness. This has happened with cancer and AIDS we dont immediately prejudge the person because of the illness and we dont consider the illness to define the person.

Using AI-based tools to destigmatize mental illness, lower treatment costs, and promise care for people who have limited access to it would be a giant leap toward mainstreaming mental health. Moreover, for therapy-resistant people, virtual therapists present a unique solution. Just as patients are more likely to open up to a chatbot, studies show that people reveal more personal, intimate information face-to-face with a humanoid-like machine than to a live human. The AI in the virtual human, in turn, is designed to sense a persons intonation, movements and gestures for clues to their mental state.

Could machines that look, act and sound human replace psychologists and psychiatrists? Probably not that possibility is limited so far by a lack of technological understanding and infrastructure but many clinicians fear this future nonetheless. Virtual therapists are available anytime, anywhere. Theyre never tired, never fatigued; they can build a giant database of what you say and how you say it, says Skip Rizzo, director for medical virtual reality at the University of Southern Californias Institute for Creative Technologies.

Rizzo and his colleagues are leaders in the research and development of virtual humans for mental health treatment, but he insists that the technology exists solely to alleviate the shortage of providers. Were not making a doc in a box, Rizzo says. Were helping a person to put a toe in the water in a safe, anonymous place where they can explore their issues.

If all this sounds like science-fiction, it isnt. Consequently, like a dystopian science-fiction story, the known rewards of AI carry unknowable risks. AI poses sobering ethical issues that psychologists and psychiatrists, along with the data scientists and companies creating the technology, are just beginning to confront. Who owns your mental health? Who has access to the data? What happens if the data is hacked? Might your record be used against you by employers, by governments?

David Luxton, a clinical psychologist and an authority on the ethics of artificial intelligence in behavioral- and mental-health care, is concerned about these questions and more. Who is controlling the technology? he says. I would be reluctant to provide private information about my mental state on a mobile app or the internet. How do you know what the company is going to be doing with that information?

More chillingly, machine-learning algorithms can be biased. Algorithms look for patterns thats how Amazon and other retailers can tell you what to buy, given what youve purchased or shown interest in. But algorithmic patterns can be harmful, making systematic errors that, for instance, favor one ethnic or cultural group over another or define your emotional state based on incomplete data and inaccurate assumptions. Before long the pattern becomes self-reinforcing in its confirmation bias, leading to unfair and unfortunate results.

Given these dangers, the time is now, Luxton says, to revise and update codes and practices to ensure that AI-based mental health tools are used ethically, with particular attention to privacy and transparency rules and laws. Weve got this stuff in our hands, Luxton adds. Where are we going to be in the next 10 years?

If you or someone you know is experiencing a mental-health crisis, the National Suicide Prevention Lifeline is available at any time at this toll-free telephone number: 1-800-273-8255.

Jonathan Burton is an editor and reporter at MarketWatch.

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Would you tell your innermost secrets to Alexa? How AI therapists could save you time and money on mental health care - MarketWatch

If it wasnt bad enough that Moores Law improvements in the density and cost of transistors is slowing. At the same time, the cost of designing chips and of the factories that are used to etch them is also on the rise. Any savings on any of these fronts will be most welcome to keep IT innovation leaping ahead.

One of the promising frontiers of research right now in chip design is using machine learning techniques to actually help with some of the tasks in the design process. We will be discussing this at our upcoming The Next AI Platform event in San Jose on March 10 with Elias Fallon, engineering director at Cadence Design Systems. (You can see the full agenda and register to attend at this link; we hope to see you there.) The use of machine learning in chip design was also one of the topics that Jeff Dean, a senior fellow in the Research Group at Google who has helped invent many of the hyperscalers key technologies, talked about in his keynote address at this weeks 2020 International Solid State Circuits Conference in San Francisco.

Google, as it turns out, has more than a passing interest in compute engines, being one of the large consumers of CPUs and GPUs in the world and also the designer of TPUs spanning from the edge to the datacenter for doing both machine learning inference and training. So this is not just an academic exercise for the search engine giant and public cloud contender particularly if it intends to keep advancing its TPU roadmap and if it decides, like rival Amazon Web Services, to start designing its own custom Arm server chips or decides to do custom Arm chips for its phones and other consumer devices.

With a certain amount of serendipity, some of the work that Google has been doing to run machine learning models across large numbers of different types of compute engines is feeding back into the work that it is doing to automate some of the placement and routing of IP blocks on an ASIC. (It is wonderful when an idea is fractal like that. . . .)

While the pod of TPUv3 systems that Google showed off back in May 2018 can mesh together 1,024 of the tensor processors (which had twice as many cores and about a 15 percent clock speed boost as far as we can tell) to deliver 106 petaflops of aggregate 16-bit half precision multiplication performance (with 32-bit accumulation) using Googles own and very clever bfloat16 data format. Those TPUv3 chips are all cross-coupled using a 3232 toroidal mesh so they can share data, and each TPUv3 core has its own bank of HBM2 memory. This TPUv3 pod is a huge aggregation of compute, which can do either machine learning training or inference, but it is not necessarily as large as Google needs to build. (We will be talking about Deans comments on the future of AI hardware and models in a separate story.)

Suffice it to say, Google is hedging with hybrid architectures that mix CPUs and GPUs and perhaps someday other accelerators for reinforcement learning workloads, and hence the research that Dean and his peers at Google have been involved in that are also being brought to bear on ASIC design.

One of the trends is that models are getting bigger, explains Dean. So the entire model doesnt necessarily fit on a single chip. If you have essentially large models, then model parallelism dividing the model up across multiple chips is important, and getting good performance by giving it a bunch of compute devices is non-trivial and it is not obvious how to do that effectively.

It is not as simple as taking the Message Passing Interface (MPI) that is used to dispatch work on massively parallel supercomputers and hacking it onto a machine learning framework like TensorFlow because of the heterogeneous nature of AI iron. But that might have been an interesting way to spread machine learning training workloads over a lot of compute elements, and some have done this. Google, like other hyperscalers, tends to build its own frameworks and protocols and datastores, informed by other technologies, of course.

Device placement meaning, putting the right neural network (or portion of the code that embodies it) on the right device at the right time for maximum throughput in the overall application is particularly important as neural network models get bigger than the memory space and the compute oomph of a single CPU, GPU, or TPU. And the problem is getting worse faster than the frameworks and hardware can keep up. Take a look:

The number of parameters just keeps growing and the number of devices being used in parallel also keeps growing. In fact, getting 128 GPUs or 128 TPUv3 processors (which is how you get the 512 cores in the chart above) to work in concert is quite an accomplishment, and is on par with the best that supercomputers could do back in the era before loosely coupled, massively parallel supercomputers using MPI took over and federated NUMA servers with actual shared memory were the norm in HPC more than two decades ago. As more and more devices are going to be lashed together in some fashion to handle these models, Google has been experimenting with using reinforcement learning (RL), a special subset of machine learning, to figure out where to best run neural network models at any given time as model ensembles are running on a collection of CPUs and GPUs. In this case, an initial policy is set for dispatching neural network models for processing, and the results are then fed back into the model for further adaptation, moving it toward more and more efficient running of those models.

In 2017, Google trained an RL model to do this work (you can see the paper here) and here is what the resulting placement looked like for the encoder and decoder, and the RL model to place the work on the two CPUs and four GPUs in the system under test ended up with 19.3 percent lower runtime for the training runs compared to the manually placed neural networks done by a human expert. Dean added that this RL-based placement of neural network work on the compute engines does kind of non-intuitive things to achieve that result, which is what seems to be the case with a lot of machine learning applications that, nonetheless, work as well or better than humans doing the same tasks. The issue is that it cant take a lot of RL compute oomph to place the work on the devices to run the neural networks that are being trained themselves. In 2018, Google did research to show how to scale computational graphs to over 80,000 operations (nodes), and last year, Google created what it calls a generalized device placement scheme for dataflow graphs with over 50,000 operations (nodes).

Then we start to think about using this instead of using it to place software computation on different computational devices, we started to think about it for could we use this to do placement and routing in ASIC chip design because the problems, if you squint at them, sort of look similar, says Dean. Reinforcement learning works really well for hard problems with clear rules like Chess or Go, and essentially we started asking ourselves: Can we get a reinforcement learning model to successfully play the game of ASIC chip layout?

There are a couple of challenges to doing this, according to Dean. For one thing, chess and Go both have a single objective, which is to win the game and not lose the game. (They are two sides of the same coin.) With the placement of IP blocks on an ASIC and the routing between them, there is not a simple win or lose and there are many objectives that you care about, such as area, timing, congestion, design rules, and so on. Even more daunting is the fact that the number of potential states that have to be managed by the neural network model for IP block placement is enormous, as this chart below shows:

Finally, the true reward function that drives the placement of IP blocks, which runs in EDA tools, takes many hours to run.

And so we have an architecture Im not going to get a lot of detail but essentially it tries to take a bunch of things that make up a chip design and then try to place them on the wafer, explains Dean, and he showed off some results of placing IP blocks on a low-powered machine learning accelerator chip (we presume this is the edge TPU that Google has created for its smartphones), with some areas intentionally blurred to keep us from learning the details of that chip. We have had a team of human experts places this IP block and they had a couple of proxy reward functions that are very cheap for us to evaluate; we evaluated them in two seconds instead of hours, which is really important because reinforcement learning is one where you iterate many times. So we have a machine learning-based placement system, and what you can see is that it sort of spreads out the logic a bit more rather than having it in quite such a rectangular area, and that has enabled it to get improvements in both congestion and wire length. And we have got comparable or superhuman results on all the different IP blocks that we have tried so far.

Note: I am not sure we want to call AI algorithms superhuman. At least if you dont want to have it banned.

Anyway, here is how that low-powered machine learning accelerator for the RL network versus people doing the IP block placement:

And here is a table that shows the difference between doing the placing and routing by hand and automating it with machine learning:

And finally, here is how the IP block on the TPU chip was handled by the RL network compared to the humans:

Look at how organic these AI-created IP blocks look compared to the Cartesian ones designed by humans. Fascinating.

Now having done this, Google then asked this question: Can we train a general agent that is quickly effective at placing a new design that it has never seen before? Which is precisely the point when you are making a new chip. So Google tested this generalized model against four different IP blocks from the TPU architecture and then also on the Ariane RISC-V processor architecture. This data pits people working with commercial tools and various levels tuning on the model:

And here is some more data on the placement and routing done on the Ariane RISC-V chips:

You can see that experience on other designs actually improves the results significantly, so essentially in twelve hours you can get the darkest blue bar, Dean says, referring to the first chart above, and then continues with the second chart above. And this graph showing the wireline costs where we see if you train from scratch, it actually takes the system a little while before it sort of makes some breakthrough insight and was able to significantly drop the wiring cost, where the pretrained policy has some general intuitions about chip design from seeing other designs and people that get to that level very quickly.

Just like we do ensembles of simulations to do better weather forecasting, Dean says that this kind of AI-juiced placement and routing of IP block sin chip design could be used to quickly generate many different layouts, with different tradeoffs. And in the event that some feature needs to be added, the AI-juiced chip design game could re-do a layout quickly, not taking months to do it.

And most importantly, this automated design assistance could radically drop the cost of creating new chips. These costs are going up exponentially, and data we have seen (thanks to IT industry luminary and Arista Networks chairman and chief technology officer Andy Bechtolsheim), an advanced chip design using 16 nanometer processes cost an average of $106.3 million, shifting to 10 nanometers pushed that up to $174.4 million, and the move to 7 nanometers costs $297.8 million, with projections for 5 nanometer chips to be on the order of $542.2 million. Nearly half of that cost has been and continues to be for software. So we know where to target some of those costs, and machine learning can help.

The question is will the chip design software makers embed AI and foster an explosion in chip designs that can be truly called Cambrian, and then make it up in volume like the rest of us have to do in our work? It will be interesting to see what happens here, and how research like that being done by Google will help.

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Google Teaches AI To Play The Game Of Chip Design - The Next Platform

Editors Note: This article was submitted in response to thecall for ideas issued by the co-chairs of the National Security Commission on Artificial Intelligence, Eric Schmidt and Robert Work. It addresses the third question (part a.), which asks how institutions, organizational structures, and infrastructure will affect AI development, and will artificial intelligence require the development of new institutions or changes to existing institutions.

Artificial intelligence (AI) is often portrayed as a single omnipotent force the computer as God. Often the AI is evil, or at least misguided. According to Hollywood, humans can outwit the computer (2001: A Space Odyssey), reason with it (Wargames), blow it up (Star Wars: The Phantom Menace), or be defeated by it (Dr. Strangelove). Sometimes the AI is an automated version of a human, perhaps a human fighters faithful companion (the robot R2-D2 in Star Wars).

These science fiction tropes are legitimate models for military discussion and many are being discussed. But there are other possibilities. In particular, machine learning may give rise to new forms of intelligence; not natural, but not really artificial if the term implies having been designed in detail by a person. Such new forms of intelligence may resemble that of humans or other animals, and we will discuss them using language associated with humans, but we are not discussing robots that have been deliberately programmed to emulate human intelligence. Through machine learning they have been programmed by their own experiences. We speculate that some of the characteristics that humans have evolved over millennia will also evolve in future AI, characteristics that have evolved purely for their success in a wide range of situations that are real, for humans, or simulated, for robots.

As the capabilities of AI-enabled robots increase, and in particular as behaviors emerge that are both complex and outside past human experience, how will we organize, train, and command them and the humans who will supervise and maintain them? Existing methods and structures, such as military ranks and doctrine, that have evolved over millennia to manage the complexity of human behavior will likely be necessary. But because robots will evolve new behaviors we cannot yet imagine, they are unlikely to be sufficient. Instead, the military and its partners will need to learn new types of organization and new approaches to training. It is impossible to predict what these will be but very possible they will differ greatly from approaches that have worked in the past. Ongoing experimentation will be essential.

How to Respond to AI Advances

The development of AI, especially machine learning, will lead to unpredictable new types of robots. Advances in AI suggest that humans will have the ability to create many types of robots, of different shapes, sizes, or degrees of independence or autonomy. It is conceivable that humans may one day be able to design tiny AI bullets to pierce only designated targets, automated aircraft to fly as loyal wingmen alongside human pilots, or thousands of AI fish to swim up an enemys river. Or we could design AI not as a device but as a global grid that analyzes vast amounts of diverse data. Multiple programs funded by the Department of Defense are on their way to developing robots with varying degrees of autonomy.

In science fiction, robots are often depicted as behaving in groups (like the robot dogs in Metalhead). Researchers inspired by animal behaviors have developed AI concepts such as swarms, in which relatively simple rules for each robot can result in complex emergent phenomena on a larger scale. This is a legitimate and important area of investigation. Nevertheless, simply imitating the known behaviors of animals has its limits. After observing the genocidal nature of military operations among ants, biologists Bert Holldobler and E. O. Wilson wrote, If ants had nuclear weapons, they would probably end the world in a week. Nor would we want to limit AI to imitating human behavior. In any case, a major point of machine learning is the possibility of uncovering new behaviors or strategies. Some of these will be very different from all past experience; human, animal, and automated. We will likely encounter behaviors that, although not human, are so complex that some human language, such as personality, may seem appropriately descriptive. Robots with new, sophisticated patterns of behavior may require new forms of organization.

Military structure and scheme of maneuver is key to victory. Groups often fight best when they dont simply swarm but execute sophisticated maneuvers in hierarchical structures. Modern military tactics were honed over centuries of experimentation and testing. This was a lengthy, expensive, and bloody process.

The development of appropriate organizations and tactics for AI systems will also likely be expensive, although one can hope that through the use of simulation it will not be bloody. But it may happen quickly. The competitive international environment creates pressure to use machine learning to develop AI organizational structure and tactics, techniques, and procedures as fast as possible.

Despite our considerable experience organizing humans, when dealing with robots with new, unfamiliar, and likely rapidly-evolving personalities we confront something of a blank slate. But we must think beyond established paradigms, beyond the computer as all-powerful or the computer as loyal sidekick.

Humans fight in a hierarchy of groups, each soldier in a squad or each battalion in a brigade exercising a combination of obedience and autonomy. Decisions are constantly made at all levels of the organization. Deciding what decisions can be made at what levels is itself an important decision. In an effective organization, decision-makers at all levels have a good idea of how others will act, even when direct communication is not possible.

Imagine an operation in which several hundred underwater robots are swimming up a river to accomplish a mission. They are spotted and attacked. A decision must be made: Should they retreat? Who decides? Communications will likely be imperfect. Some mid-level commander, likely one of the robot swimmers, will decide based on limited information. The decision will likely be difficult and depend on the intelligence, experience, and judgment of the robot commander. It is essential that the swimmers know who or what is issuing legitimate orders. That is, there will have to be some structure, some hierarchy.

The optimal unit structure will be worked out through experience. Achieving as much experience as possible in peacetime is essential. That means training.

Training Robot Warriors

Robots with AI-enabled technologies will have to be exercised regularly, partly to test them and understand their capabilities and partly to provide them with the opportunity to learn from recreating combat. This doesnt mean that each individual hardware item has to be trained, but that the software has to develop by learning from its mistakes in virtual testbeds and, to the extent that they are feasible, realistic field tests. People learn best from the most realistic training possible. There is no reason to expect machines to be any different in that regard. Furthermore, as capabilities, threats, and missions evolve, robots will need to be continuously trained and tested to maintain effectiveness.

Training may seem a strange word for machine learning in a simulated operational environment. But then, conventional training is human learning in a controlled environment. Robots, like humans, will need to learn what to expect from their comrades. And as they train and learn highly complex patterns, it may make sense to think of such patterns as personalities and memories. At least, the patterns may appear that way to the humans interacting with them. The point of such anthropomorphic language is not that the machines have become human, but that their complexity is such that it is helpful to think in these terms.

One big difference between people and machines is that, in theory at least, the products of machine learning, the code for these memories or personalities, can be uploaded directly from one very experienced robot to any number of others. If all robots are given identical training and the same coded memories, we might end up with a uniformity among a units members that, in the aggregate, is less than optimal for the unit as a whole.

Diversity of perspective is accepted as a valuable aid to human teamwork. Groupthink is widely understood to be a threat. Its reasonable to assume that diversity will also be beneficial to teams of robots. It may be desirable to create a library of many different personalities or memories that could be assigned to different robots for particular missions. Different personalities could be deliberately created by using somewhat different sets of training testbeds to develop software for the same mission.

If AI can create autonomous robots with human-like characteristics, what is the ideal personality mix for each mission? Again, we are using the anthropomorphic term personality for the details of the robots behavior patterns. One could call it a robots programming if that did not suggest the existence of an intentional programmer. The robots personalities have evolved from the robots participation in a very large number of simulations. It is unlikely that any human will fully understand a given personality or be able to fully predict all aspects of a robots behavior.

In a simple case, there may be one optimum personality for all the robots of one type. In more complicated situations, where robots will interact with each other, having robots that respond differently to the same stimuli could make a unit more robust. These are things that military planners can hope to learn through testing and training. Of course, attributes of personality that may have evolved for one set of situations may be less than optimal, or positively dangerous, in another. We talk a lot about artificial intelligence. We dont discuss artificial mental illness. But there is no reason to rule it out.

Of course, humans will need to be trained to interact with the machines. Machine learning systems already often exhibit sophisticated behaviors that are difficult to describe. Its unclear how future AI-enabled robots will behave in combat. Humans, and other robots, will need experience to know what to expect and to deal with any unexpected behaviors that may emerge. Planners need experience to know which plans might work.

But the human-robot relationship might turn out to be something completely different. For all of human history, generals have had to learn their soldiers capabilities. They knew best exactly what their troops could do. They could judge the psychological state of their subordinates. They might even know when they were being lied to. But todays commanders do not know, yet, what their AI might prove capable of. In a sense, it is the AI troops that will have to train their commanders.

In traditional military services, the primary peacetime occupation of the combat unit is training. Every single servicemember has to be trained up to the standard necessary for wartime proficiency. This is a huge task. In a robot unit, planners, maintainers, and logisticians will have to be trained to train and maintain the machines but may spend little time working on their hardware except during deployment.

What would the units look like? What is the optimal unit rank structure? How does the human rank structure relate to the robot rank structure? There are a million questions as we enter uncharted territory. The way to find out is to put robot units out onto test ranges where they can operate continuously, test software, and improve machine learning. AI units working together can learn and teach each other and humans.

Conclusion

AI-enabled robots will need to be organized, trained, and maintained. While these systems will have human-like characteristics, they will likely develop distinct personalities. The military will need an extensive training program to inform new doctrines and concepts to manage this powerful, but unprecedented, capability.

Its unclear what structures will prove effective to manage AI robots. Only by continuous experimentation can people, including computer scientists and military operators, understand the developing world of multi-unit human and robot forces. We must hope that experiments lead to correct solutions. There is no guarantee that we will get it right. But there is every reason to believe that as technology enables the development of new and more complex patterns of robot behavior, new types of military organizations will emerge.

Thomas Hamilton is a Senior Physical Scientist at the nonprofit, nonpartisan RAND Corporation. He has a Ph.D. in physics from Columbia University and was a research astrophysicist at Harvard, Columbia, and Caltech before joining RAND. At RAND he has worked extensively on the employment of unmanned air vehicles and other technology issues for the Defense Department.

Image: Wikicommons (U.S. Air Force photo by Kevin L. Moses Sr.)

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How to Train Your AI Soldier Robots (and the Humans Who Command Them) - War on the Rocks

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Machine learning algorithms are often categorized as supervised or unsupervised.

Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

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What is Machine Learning? A definition - Expert System

TURIN, Italy--(BUSINESS WIRE)--The European Central Bank (ECB), in collaboration with Reply, leader in digital technology innovation, is organising the Supervisory Data Hackathon, a coding marathon focussing on the application of Machine Learning and Artificial Intelligence.

From 27 to 29 February 2020, at the ECB in Frankfurt, more than 80 participants from the ECB, Reply and further companies explore possibilities to gain deeper and faster insights into the large amount of supervisory data gathered by the ECB from financial institutions through regular financial reporting for risk analysis. The coding marathon provides a protected space to co-creatively develop new ideas and prototype solutions based on Artificial Intelligence within a short timeframe.

Ahead of the event, participants submit projects in the areas of data quality, interlinkages in supervisory reporting and risk indicators. The most promising submissions will be worked on for 48 hours during the event by the multidisciplinary teams composed of members from the ECB, Reply and other companies.

Reply has proven its Artificial Intelligence and Machine Learning capabilities with numerous projects in various industries and combines this technological expertise with in-depth knowledge of the financial services industry and its regulatory environment.

Coding marathons using the latest technologies are a substantial element in Replys toolset for sparking innovation through training and knowledge transfer internally and with clients and partners.

Reply Reply [MTA, STAR: REY] specialises in the design and implementation of solutions based on new communication channels and digital media. As a network of highly specialised companies, Reply defines and develops business models enabled by the new models of big data, cloud computing, digital media and the internet of things. Reply delivers consulting, system integration and digital services to organisations across the telecom and media; industry and services; banking and insurance; and public sectors. http://www.reply.com

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REPLY: European Central Bank Explores the Possibilities of Machine Learning With a Coding Marathon Organised by Reply - Business Wire

Speechmatics and Soho2 have today announced their partnership to deliver consulting services to their customers, and a new product offering Speech2.

Soho2 has significant depth in delivering machine-learning driven solutions to market. The new product from Soho2 will give companies in legal, compliance and contact centers the invaluable ability to analyze voice data garnered from calls. Speech2 enables companies to bring new levels of flexibility to data analysis for high-volume, real time or recorded voice data through mission-critical, accurate speech recognition.

Using AI and machine learning, the solution will deliver an unparalleled ability to derive insight from voice data and also manage risk. The product can be deployed in any customer-managed environment to enable control over personal or sensitive data to be retained.

As part of the new product offering, Speechmatics - a UK leader in any context speech recognition technology - will transcribe voice data into accurate, contextual understanding for analysis. Speech2 will allow businesses to identify and address risks, as well as pinpoint missing sales opportunities. The product can also identify cases of fraud, while the legal industry can identify risks with the data, and even aid with event reconstruction.

George Tziahanas, Managing Partner of Soho2, said: Our experience demonstrates the potential for great innovation in machine learning, delivering huge commercial value to enterprises across industries. We teamed up with Speechmatics to ensure our latest services and product deliver the best speech recognition technology on the market. The partnership enables us to innovate with voice securely which is crucial to our customers and industries.

Jeff Palmer, VP of Sales at Speechmatics, added: Speech2 will deliver unparalleled insights and risk management abilities, using Speechmatics any-context speech recognition engine. Soho2 also brings depth in services that deliver high-value machine learning solutions, which will benefit their customer-base. Were excited to be working with Soho2 and seeing how their customers derive value from their voice data and view it with a renewed sense of curiosity.

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Speechmatics and Soho2 apply machine learning to analyse voice data - Finextra