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The NFL and Amazon announced an expansion of their partnership at their annual AWS re:Invent ... [+] conference in Las Vegas that will use artificial intelligence and machine learning to combat player injuries. (Photo by Michael Zagaris/San Francisco 49ers/Getty Images)

Injury prevention in sports is one of the most important issues facing a number of leagues. This is particularly true in the NFL, due to the brutal nature of that punishing sport, which leaves many players sidelined at some point during the season. A number of startups are utilizing technology to address football injury issues, specifically limiting the incidence of concussions. Now, one of the largest companies in the world is working with the league in these efforts.

A week after partnering with the Seattle Seahawks on its machine learning/artificial intelligence offerings, Amazon announced a partnership Thursday in which the technology giant will use those same tools to combat football injuries. Amazon has been involved with the league, with its Next Gen Stats partnership, and now the two companies will work to advance player health and safety as the sport moves forward after its 100th season this year. Amazons AWS cloud services will use its software to gather and analyze large volumes of player health data and scan video images with the objective of helping teams treat injuries and rehabilitate players more effectively. The larger goal will be to create a new Digital Athlete platform to anticipate injury before it even takes place.

This partnership expands the quickly growing relationship between the NFL and Amazon/AWS. as the two have already teamed up for two years with the leagues Thursday Night Football games streamed on the companys Amazon Prime Video platform. Amazon paid $130 million for rights that run through next season. The league also uses AWSs ML Solutions Lab,as well as Amazons SageMaker platform, that enables data scientists and developers to build and develop machine learning models that can also lead to the leagues ultimate goal of predicting and limiting player injury.

The NFL is committed to re-imagining the future of football, said NFL Commissioner Roger Goodell. When we apply next-generation technology to advance player health and safety, everyone wins from players to clubs to fans. The outcomes of our collaboration with AWS and what we will learn about the human body and how injuries happen could reach far beyond football. As we look ahead to our next 100 seasons, were proud to partner with AWS in that endeavor.

The new initiative was announced as part of Amazons AWS re:Invent conference in Las Vegas on Thursday. Among the technologies that AWS and the league announced in its Digital Athlete platform is a computer-simulated model of an NFL player that will model infinite scenarios within NFL gameplay in order to identify a game environment that limits the risk to a player. Digital Athlete uses Amazons full arsenal of technologies, including the AI, ML and computer vision technology that is used with Amazons Rekognition tool and that uses enormous data sets encompassing historical and more modern video to identify a wide variety of solutions, including the prediction of player injury.

By leveraging the breadth and depth of AWS services, the NFL is growing its leadership position in driving innovation and improvements in health and player safety, which is good news not only for NFL players but also for athletes everywhere, said Andy Jassy, CEO of AWS. This partnership represents an opportunity for the NFL and AWS to develop new approaches and advanced tools to prevent injury, both in and potentially beyond football.

These announcements come at a time when more NFL players are utilizing their large platforms to bring awareness to injuries and the enormous impact those injuries have on their bodies. Former New England Patriots tight end Rob Gronkowski has been one of the most productive NFL players at his position in league history but he had to retire from the league this year, at the age of 29, due to a rash of injuries.

The future Hall of Fame player estimated that he suffered probably 20 concussions in his football career. These admissions have significant consequences on youth participation rates in the sport. Partnerships like the one announced yesterday will need to be successful in order for the sport to remain on solid footing heading into the new decade.

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The NFL And Amazon Want To Transform Player Health Through Machine Learning - Forbes

Jen-Hsun Huang, president and chief executive officer of Nvidia Corp., gestures as he speaks during ... [+] the company's event at the 2019 Consumer Electronics Show (CES) in Las Vegas, Nevada, U.S., on Sunday, Jan. 6, 2019. CES showcases more than 4,500 exhibiting companies, including manufacturers, developers and suppliers of consumer technology hardware, content, technology delivery systems and more. Photographer: David Paul Morris/Bloomberg

We found that if Nvidia Stock drops 10% or more in a week (5 trading days), there is a solid 36% chance itll recover 10% or more, over the next month (about 20 trading days)

Nvidia stock has seen significant volatility this year. While the company has been impacted by the broader correction in the semiconductor space and the trade war between the U.S. and China, the stock is being supported by a strong long-term outlook for GPU demand amid growing applications in Deep Learning and Artificial Intelligence.

Considering the recent price swings, we started with a simple question that investors could be asking about Nvidia stock: given a certain drop or rise, say a 10% drop in a week, what should we expect for the next week? Is it very likely that the stock will recover the next week? What about the next month or a quarter? You can test a variety of scenarios on the Trefis Machine Learning Engine to calculate if Nvidia stock dropped, whats the chance itll rise.

For example, after a 5% drop over a week (5 trading days), the Trefis machine learning engine says chances of an additional 5% drop over the next month, are about 40%. Quite significant, and helpful to know for someone trying to recover from a loss. Knowing what to expect for almost any scenario is powerful. It can help you avoid rash moves. Given the recent volatility in the market, the mix of macroeconomic events (including the trade war with China and interest rate easing by the U.S. Fed), we think investors can prepare better.

Below, we also discuss a few scenarios and answer common investor questions:

Question 1: Does a rise in Nvidia stock become more likely after a drop?

Answer:

Not really.

Specifically, chances of a 5% rise in Nvidia stock over the next month:

= 40%% after Nvidia stock drops by 5% in a week.

versus,

= 44.5% after Nvidia stock rises by 5% in a week.

Question 2: What about the other way around, does a drop in Nvidia stock become more likely after a rise?

Answer:

No.

Specifically, chances of a 5% decline in Nvidia stock over the next month:

= 40% after NVIDIA stock drops by 5% in a week

versus,

= 27% after NVIDIA stock rises by 5% in a week

Question 3: Does patience pay?

Answer:

According to data and Trefis machine learning engines calculations, largely yes!

Given a drop of 5% in Nvidia stock over a week (5 trading days), while there is only about 28% chance the Nvidia stock will gain 5% over the subsequent week, there is more than 58% chance this will happen in 6 months.

The table below shows the trend:

Trefis

Question 4: What about the possibility of a drop after a rise if you wait for a while?

Answer:

After seeing a rise of 5% over 5 days, the chances of a 5% drop in Nvidia stock are about 30% over the subsequent quarter of waiting (60 trading days). However, this chance drops slightly to about 29% when the waiting period is a year (250 trading days).

Whats behind Trefis? See How Its Powering New Collaboration and What-Ifs ForCFOs and Finance Teams|Product, R&D, and Marketing Teams More Trefis Data Like our charts? Exploreexample interactive dashboardsand create your own

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Machine Learning Answers: If Nvidia Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? - Forbes

Americas National Football league is turning to emerging technology to try to solve its ongoing challenges around player safety. The sports governing body says it has amassed huge amounts of data but wants to apply machine learning to gain better insights and predictive capabilities.

It is hoped the insights will inform new rules, safer equipment, and better injury rehabilitation methods. However, the data will not be available to independent researchers.

Last week the NFL announced a partnership with Amazon Web Services to provide the digital services including machine learning and digital twin applications. Terms of the deal were not disclosed.

As the NFL has reached hyper professionalisation, data suggests player injuries have worsened, particularly head injuries sustained through high impact collisions. Several retired players have been diagnosed with or report symptoms of chronic traumatic encephalopathy, a neurodegenerative disease which can only be fully diagnosed post mortem.

As scrutiny has grown the NFL has responded with several rule changes and redesigning player helmets, both initiatives which it says has reduced concussions. However the league was also accused of failing to notify players of the links between concussions and brain injuries.

All of our initiatives on the health and safety side started with the engineering roadmap around minimising head impact on field, NFL executive vice president, Jeff Miller told Which-50 following the announcement.

Miller who is responsible for player health and safety, said the new technology is a new opportunity to minimise risk to players.

I think the speed, the pace of the insights that are available as a result of this [technology] are going to continue towards that same goal, hopefully in a much more efficient, and in fact mature, faster supersized scale.

Miller said the NFL has a responsibility to pass on the insights to lower levels of the game like high school and youth leagues. However, the data will not be available to external researchers initially.

As we find those insights I think were going to be able to share those, were going to be able to share those within the sport and hopefully over time outside of the sport as well.

NFL commissioner Roger Goodell announced the AWS deal, which builds on an existing partnership for game statistics, alongside Andy Jassy, the public cloud providers CEO, during the AWS:re:invent conference in Las Vegas last week.

Goodell said the NFL had amassed huge amounts of data from sensors and video feeds but needed the AWS tools to better leverage it.

When you take the combination of that the possibilities are enormous, the NFL boss said. We want to use the data to change the game. There are very few relationships we get involved with where the partner and the NFL can change the game.

When we apply next-generation technology to advance player health and safety, everyone wins from players to clubs to fans.

AWS machine learning tools will be applied to the data to help build a digital athlete, a type of digital twin which can be used to simulate certain scenarios including impacts.

The outcomes of our collaboration with AWS and what we will learn about the human body and how injuries happen could reach far beyond football, he said.

The author traveled to AWS re:Invent as a guest of Amazon.

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NFL Looks to Cloud and Machine Learning to Improve Player Safety - Which-50

Enlarge / Machine learning is really good at turning pictures of normal things into pictures of eldritch horrors.

Jim Salter

Artificial Intelligenceor, if you prefer, Machine Learningis today's hot buzzword. Unlike many buzzwords have come before it, though, this stuff isn't vaporware dreamsit's real, it's here already, and it's changing your life whether you realize it or not.

Before we go too much further, let's talk quickly about that term "Artificial Intelligence." Yes, it's warranted; no, it doesn't mean KITT from Knight Rider, or Samantha, the all-too-human unseen digital assistant voiced by Scarlett Johansson in 2013'sHer. Aside from being fictional, KITT and Samantha are examples ofstrong artificial intelligence, also known as Artificial General Intelligence (AGI). On the other hand, artificial intelligencewithout the "strong" or "general" qualifiersis an established academic term dating back to the 1955 proposal for the Dartmouth Summer Project on Artificial Intelligence (DSRPAI), written by Professors John McCarthy and Marvin Minsky.

All "artificial intelligence" really means is a system that emulates problem-solving skills normally seen in humans or animals. Traditionally, there are two branches of AIsymbolic and connectionist. Symbolic means an approach involving traditional rules-based programminga programmer tells the computer what to expect and how to deal with it, very explicitly. The "expert systems" of the 1980s and 1990s were examples of symbolic (attempts at) AI; while occasionally useful, it's generally considered impossible to scale this approach up to anything like real-world complexity.

NBCUniversal

Artificial Intelligence in the commonly used modern sense almost always refers to connectionist AI. Connectionist AI, unlike symbolic AI, isn't directly programmed by a human. Artificial neural networks are the most common type of connectionist AI, also sometimes referred to as machine learning. My colleague Tim Lee just got done writing about neural networks last weekyou can get caught up right here.

If you wanted to build a system that could drive a car, instead of programming it directly you might attach a sufficiently advanced neural network to its sensors and controls, and then let it "watch" a human driving for tens of thousands of hours. The neural network begins to attach weights to events and patterns in the data flow from its sensors that allow it to predict acceptable actions in response to various conditions. Eventually, you might give the network conditional control of the car's controls and allow it to accelerate, brake, and steer on its ownbut still with a human available. The partially trained neural network can continue learning in response to when the human assistant takes the controls away from it. "Whoops, shouldn't have donethat," and the neural network adjusts weighted values again.

Sounds very simple, doesn't it? In practice, not so muchthere are many different types of neural networks (simple, convolutional, generative adversarial, and more), and none of them is very bright on its ownthe brightest is roughly similar in scale to a worm's brain. Most complex, really interesting tasks will require networks of neural networks that preprocess data to find areas of interest, pass those areas of interest onto other neural networks trained to more accurately classify them, and so forth.

One last piece of the puzzle is that, when dealing with neural networks, there are two major modes of operation: inference and training. Training is just what it sounds likeyou give the neural network a large batch of data that represents a problem space, and let it chew through it, identifying things of interest and possibly learning to match them to labels you've provided along with the data. Inference, on the other hand, is using an already-trained neural network to give you answers in a problem space that it understands.

Both inference and training workloads can operate several orders of magnitude more rapidly on GPUs than on general-purpose CPUsbut that doesn't necessarily mean you want to do absolutely everything on a GPU. It's generally easier and faster to runsmall jobs directly on CPUs rather than invoking the initial overhead of loading models and data into a GPU and its onboard VRAM, so you'll very frequently see inference workloads run on standard CPUs.

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Cloudy with a chance of neurons: The tools that make neural networks work - Ars Technica

Bots are a lot like humans: Some are cute. Some are ugly. Some are harmless. Some are menacing. Some are friendly. Some are annoying ... and a little racist. Bots serve their creators and society as helpers, spies, educators, servants, lab technicians, and artists. Sometimes, they save lives. Occasionally, they destroy them.

In the 2010s, automation got better, cheaper, and way less avoidable. Its still mysterious, but no longer foreign; the most Extremely Online among us interact with dozens of AIs throughout the day. That means driving directions are more reliable, instant translations are almost good enough, and everyone gets to be an adequate portrait photographer, all powered by artificial intelligence. On the other hand, each of us now sees a personalized version of the world that is curated by an AI to maximize engagement with the platform. And by now, everyone from fruit pickers to hedge fund managers has suffered through headlines about being replaced.

Humans and tech have always coexisted and coevolved, but this decade brought us closer togetherand closer to the futurethan ever. These days, you dont have to be an engineer to participate in AI projects; in fact, you have no choice but to help, as youre constantly offering your digital behavior to train AIs.

So heres how we changed our bots this decade, how they changed us, and where our strange relationship is going as we enter the 2020s.

All those little operational tweaks in our day come courtesy of a specific scientific approach to AI called machine learning, one of the most popular techniques for AI projects this decade. Thats when AI is tasked not only with finding the answers to questions about data sets, but with finding the questions themselves; successful deep learning applications require vast amounts of data and the time and computational power to self-test over and over again.

Deep learning, a subset of machine learning, uses neural networks to extract its own rules and adjust them until it can return the right results; other machine learning techniques might use Bayesian networks, vector maps, or evolutionary algorithms to achieve the same goal.

In January, Technology Reviews Karen Hao released an exhaustive analysis of recent papers in AI that concluded that machine learning was one of the defining features of AI research this decade. Machine learning has enabled near-human and even superhuman abilities in transcribing speech from voice, recognizing emotions from audio or video recordings, as well as forging handwriting or video, Hao wrote. Domestic spying is now a lucrative application for AI technologies, thanks to this powerful new development.

Haos report suggests that the age of deep learning is finally drawing to a close, but the next big thing may have already arrived. Reinforcement learning, like generative adversarial networks (GANs), pits neural nets against one another by having one evaluate the work of the other and distribute rewards and punishments accordinglynot unlike the way dogs and babies learn about the world.

The future of AI could be in structured learning. Just as young humans are thought to learn their first languages by processing data input from fluent caretakers with their internal language grammar, computers can also be taught how to teach themselves a taskespecially if the task is to imitate a human in some capacity.

This decade, artificial intelligence went from being employed chiefly as an academic subject or science fiction trope to an unobtrusive (though occasionally malicious) everyday companion. AIs have been around in some form since the 1500s or the 1980s, depending on your definition. The first search indexing algorithm was AltaVista in 1995, but it wasnt until 2010 that Google quietly introduced personalized search results for all customers and all searches. What was once background chatter from eager engineers has now become an inescapable part of daily life.

One function after another has been turned over to AI jurisdiction, with huge variations in efficacy and consumer response. The prevailing profit model for most of these consumer-facing applications, like social media platforms and map functions, is for users to trade their personal data for minor convenience upgrades, which are achieved through a combination of technical power, data access, and rapid worker disenfranchisement as increasingly complex service jobs are doubled up, automated away, or taken over by AI workers.

The Harvard social scientist Shoshana Zuboff explained the impact of these technologies on the economy with the term surveillance capitalism. This new economic system, she wrote, unilaterally claims human experience as free raw material for translation into behavioural data, in a bid to make profit from informed gambling based on predicted human behavior.

Were already using machine learning to make subjective decisionseven ones that have life-altering consequences. Medical applications are only some of the least controversial uses of artificial intelligence; by the end of the decade, AIs were locating stranded victims of Hurricane Maria, controlling the German power grid, and killing civilians in Pakistan.

The sheer scope of these AI-controlled decision systems is why automation has the potential to transform society on a structural level. In 2012, techno-socialist Zeynep Tufekci pointed out the presence on the Obama reelection campaign of an unprecedented number of data analysts and social scientists, bringing the traditional confluence of marketing and politics into a new age.

Intelligence that relies on data from an unjust world suffers from the principle of garbage in, garbage out, futurist Cory Doctorow observed in a recent blog post. Diverse perspectives on the design team would help, Doctorow wrote, but when it comes to certain technology, there might be no safe way to deploy:

It doesnt help that data collection for image-based AI has so far taken advantage of the most vulnerable populations first. The Facial Recognition Verification Testing Program is the industry standard for testing the accuracy of facial recognition tech; passing the program is imperative for new FR startups seeking funding.

But the datasets of human faces that the program uses are sourced, according to a report from March, from images of U.S. visa applicants, arrested people who have since died, and children exploited by child pornography. The report found that the majority of data subjects were people who had been arrested on suspicion of criminal activity. None of the millions of faces in the programs data sets belonged to people who had consented to this use of their data.

State-level efforts to regulate AI finally emerged this decade, with some success. The European Unions General Data Protection Regulation (GDPR), enforceable from 2018, limits the legal uses of valuable AI training datasets by defining the rights of the data subject (read: us); the GDPR also prohibits the black box model for machine learning applications, requiring both transparency and accountability on how data are stored and used. At the end of the decade, Google showed the class how not to regulate when they built, and then scrapped, an external AI ethics panel a week later, feigning shock at all the negative reception.

Even attempted regulation is a good sign. It means were looking at AI for what it is: not a new life form that competes for resources, but as a formidable weapon. Technological tools are most dangerous in the hands of malicious actors who already hold significant power; you can always hire more programmers. During the long campaign for the 2016 U.S. presidential election, the Putin-backed IRA Twitter botnet campaignsessentially, teams of semi-supervised bot accounts that spread disinformation on purpose and learn from real propagandainfiltrated the very mechanics of American democracy.

Keeping up with AI capacities as they grow will be a massive undertaking. Things could still get much, much worse before they get better; authoritarian governments around the world have a tendency to use technology to further consolidate power and resist regulation.

Tech capabilities have long since proved too fast for traditional human lawmakers, but one hint of what the next decade might hold comes from AIs themselves, who are beginning to be deployed as weapons against the exact type of disinformation other AIs help to create and spread. There now exists, for example, a neural net devoted explicitly to the task of identifying neural net disinformation campaigns on Twitter. The neural nets name is Grover, and its really good at this.

Continued here:

The Bot Decade: How AI Took Over Our Lives in the 2010s - Popular Mechanics