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Here Are This Weeks Top Stories:1. Collaboration: Zooms Stock Is Skyrocketing But Is It Secure? 2. Security: Healthcare Hit By COVID-19 Cyber Attack 3. IT Strategy: Washington Signs Facial Tech Into Law Zoom's Stock Is Skyrocketing But Is It Secure?

Even as Zoom's active user count scales everyday with U.S. volumes touching 4.84 million on Monday, concerns around the solutions's security credentials have risen significantly. Elon Musk founded SpaceX shunned the videoconferencing app, citing significant privacy and security concerns. New York's Attorney General wrote to Zoom about its ability to secure massive workloads.

Big Picture: While stock is soaring for Zoom amid a global meltdown, the lack of end-to-end encryption will impact user growth. "It is not possible to enable E2E encryption for Zoom video meetings", Zoom spokesperson reportedly shared with The Intercept. Even though Zoom secures audio and video meetings using TCP and UDP connections, it can access unencrypted video and audio content of meetings. Another downsize Zoom sells user data to advertisers for targeted marketing.

Our Take: Considering that NASA has prohibited its employees from using Zoom and the FBI has observed instances of people invading school sessions on the service, organizations need to prevent employees from sharing links to team meetings publicly. Alternatively, organizations can also try other video conferencing services that boast better security features.

The coronavirus epidemic is weighing heavily on the security sector with a record spike in COVID-19 themed cyberattacks. In fact, the healthcare industry on the frontlines of the epidemic is facing a record surge of cyber attacks. As per reports, hackers targeted U.K. based Hammersmith Medicines Research, the test center preparing to perform medical trials on prospective COVID-19 vaccines. The test center was hit by a cyber attack on March 14 when hackers attempted to breach the system. Reports indicate some data was stolen and posted online for ransom. Additionally, Security researchers from Nokia's Threat Intelligence Lab uncovered a powerful malware disguised as a "coronavirus map" application that infects Windows computers and is disguised as software from John Hopkins University.

Big Picture: The coronavirus epidemic has become the new attack vector for cyber criminals who have jumped on the opportunity. The Coronavirus map app is one such malicious app- secretly stealing credit card numbers, browser history, cookies, usernames and passwords from the browser's cache without users noticing such actions.

Our Take: Cybercriminals are exploiting global concerns around COVID-19, targeting people and organizations on the front lines of the pandemic. The record increase in hacking attempts has prompted cybersecurity professionals to step up the plate and form a response group called Cyber Volunteers 19.

Both individuals and corporations need to put more guardrails against these cyber threats and ensure appropriate security frameworks and policies to keep threat actors at bay.

On Tuesday, the Washington state legislature passed a bill into law to regulate the use of facial recognition by government agencies. As per the new law, facial recognition technologies need to be regularly tested for fairness and accuracy and can only be used under warrant. However, another bill to regulate the commercial use of facial recognition was tabled but not passed.

Big Picture: Washington tech giant Microsoft that has been lobbying for regulations around the use of facial recognition tech welcomed the move. Microsoft President Brad Smith hailed the new law as a significant breakthrough and an early and important model to serve the public interest without impacting people's fundamental rights.

Our Take: Facial recognition offers many benefits but also poses a serious threat to privacy and security. Ethical use of such technologies should be enforced through legislation and should apply to both public and private entities. The scope and purposes of facial recognition tech should also be reviewed regularly to prevent misuse.

AlphaGo Developer Nabs ACM Prize!

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Latest Tech News This Week: Zoom Hit With Security Woes, Cyber Attacks on Healthcare Ramp Up|Weekly Rundown - Toolbox

Investment Opportunities

By Nicholas Vardy

Originally posted April 2, 2020 on Liberty Through Wealth

Editors Note: We know things are changing rapidly as the number of COVID-19 cases increases and Mr. Market reacts. Our strategists are here for you to keep you up to date with all the information that you need to make smart investment choices. Take a look at Nicholas Vardys latest video update here: How to Manage Financial Risks During Pandemic.

Christina Grieves, Senior Managing Editor

Machines are taking over Wall Street.

Today, the biggest quant investing firms, like Renaissance Technologies, Two Sigma Investments and D.E. Shaw, manage tens of billions of dollars.

In total, quant-focused hedge funds manage almost $1 trillion in assets.

The rise of quant investing has Wall Streets army of human financial analysts rightfully worried about their jobs.

Picture a room full of financial analysts spending their days (and nights) sifting through company balance sheets, income statements, news stories and regulatory filings. All this to unearth a yet undiscovered investment opportunity.

Compare that image with lightning-fast computers sifting through millions of patent databases, academic journals and social media posts every single day.

We humans dont have a prayer.

But thanks to the democratization of computing power, the rise of quant investing is terrific news for you, the small investor.

When I started my investment career in the 1990s, quant investing was about identifying momentum in stocks, riding trending prices like a surfer rides a wave.

I developed my first quant-based trading system in 1994 using a now-defunct computer program named Windows on Wall Street.

Today, cutting-edge quant hedge funds use computers and algorithms unimaginable two decades ago.

This kind of trading requires more the skills of astrophysics PhDs than those of traditional financial analysts.

Over the past decade, this quant-driven approach to trading has exploded. Thats partially because any edge stemming from fundamental research has all but disappeared.

Its said that in 1815, Nathan Mayer Rothschild used carrier pigeons to learn about the outcome of the Battle of Waterloo ahead of other investors. That edge made him a fortune.

George Soros attributed his early success investing in European companies in the 1960s to being a one-eyed king among the blind.

Today, financial traders have more information on their smartphones than the worlds top hedge funds did 20 years ago.

Being a one-eyed king just doesnt cut it anymore.

Trading is not the only arena in which humans have lost out to machines.

The battle between man and machine had a watershed moment in 1997. Thats when Garry Kasparov, the worlds top-ranked chess player at the time, lost to IBM supercomputer Deep Blue.

There have been many other such moments since. In 2013, IBMs Watson beat two Jeopardy champions. In 2017, Googles AlphaGo computer defeated the worlds top player in Go, humankinds most complicated board game.

In his book Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins, Kasparov concedes that human players have no chance against todays powerful computers.

The reason?

Computers follow the rules without fail. They can process vast swaths of information at the speed of light. They dont get tired. They are never off their game.

A human chess player has to screw up only once to lose a match.

The same applies to human decision making versus quant algorithms in the world of investing.

Fatigue, emotion and limited capacity to process information are all enemies to traders. In contrast, quant algorithms never tire, never get exasperated, and are immune to both a traders and Mr. Markets mood swings.

Thats why investing against machines is like playing chess against a computer.

Yes, you may beat the computer occasionally. But in the long term, its a losers game.

Quant investing may scare you.

It shouldnt.

As with all disruptive technologies, quant investing democratizes investing in unimaginable ways.

Twenty years ago, only the worlds top hedge funds had the computer power to generate consistent market-beating returns.

Today, I have access to computer programs that can develop similar quant strategies without the need for an army of PhDs. I can harness these computers to develop a wide range of quant strategies.

These strategies can unearth value, growth and high-quality companies They can focus on short-, medium- and long-term trading strategies They can identify technical factors like relative strength, momentum and reversion to the mean.

I have spent the last six months developing just such quant strategies. Specifically, I developed a short-term swing trading system.

Swing trading

Look for more information on my new trading service, Oxford Swing Trader, in the weeks ahead.

Good investing,

Nicholas

Stay informed with the latest news from Nicholas, including video updates where he shares his views on the current state of the markets. Simply like his Facebook page and follow @NickVardy on Twitter.

An accomplished investment advisor and widely recognized expert on quantitative investing, global investing and exchange-traded funds, Nicholas has been a regular commentator on CNN International and Fox Business Network. He has also been cited inTheWall Street Journal,Financial Times,Newsweek, Fox Business News, CBS, MarketWatch, Yahoo Finance and MSN Money Central. Nicholas holds a bachelors and a masters from Stanford University and a J.D. from Harvard Law School. Its no wonder his groundbreaking content is published regularly in the free daily e-letterLiberty Through Wealth.

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Quant Investing: Welcome to the Revolution - Investment U

Geoffrey Hinton, center. talks about what future deep learning neural nets may look like, flanked by Yann LeCun of Facebook, left, and Yoshua Bengio of Montreal's MILA institute for AI, during a press conference at the 34th annual AAAI conference on artificial intelligence.

The rise of dedicated chips and systems for artificial intelligence will "make possible a lot of stuff that's not possible now," said Geoffrey Hinton, the University of Toronto professor who is one of the godfathers of the "deep learning" school of artificial intelligence, during a press conference on Monday.

Hinton joined his compatriots, Yann LeCun of Facebook and Yoshua Bengio of Canada's MILA institute, fellow deep learning pioneers, in an upstairs meeting room of the Hilton Hotel on the sidelines of the 34th annual conference on AI by the Association for the Advancement of Artificial Intelligence. They spoke for 45 minutes to a small group of reporters on a variety of topics, including AI ethics and what "common sense" might mean in AI. The night before, all three had presented their latest research directions.

Regarding hardware, Hinton went into an extended explanation of the technical aspects that constrain today's neural networks. The weights of a neural network, for example, have to be used hundreds of times, he pointed out, making frequent, temporary updates to the weights. He said the fact graphics processing units (GPUs) have limited memory for weights and have to constantly store and retrieve them in external DRAM is a limiting factor.

Much larger on-chip memory capacity "will help with things like Transformer, for soft attention," said Hinton, referring to the wildly popular autoregressive neural network developed at Google in 2017. Transformers, which use "key/value" pairs to store and retrieve from memory, could be much larger with a chip that has substantial embedded memory, he said.

Also: Deep learning godfathers Bengio, Hinton, and LeCun say the field can fix its flaws

LeCun and Bengio agreed, with LeCun noting that GPUs "force us to do batching," where data samples are combined in groups as they pass through a neural network, "which isn't efficient." Another problem is that GPUs assume neural networks are built out of matrix products, which forces constraints on the kind of transformations scientists can build into such networks.

"Also sparse computation, which isn't convenient to run on GPUs ...," said Bengio, referring to instances where most of the data, such as pixel values, may be empty, with only a few significant bits to work on.

LeCun predicted that new hardware would lead to "much bigger neural nets with sparse activations," and he and Bengio both emphasized that there is an interest in doing the same amount of work with less energy. LeCun defended AI against claims it is an energy hog, however. "This idea that AI is eating the atmosphere, it's just wrong," he said. "I mean, just compare it to something like raising cows," he continued. "The energy consumed by Facebook annually for each Facebook user is 1,500-watt hours," he said. Not a lot, in his view, compared to other energy-hogging technologies.

The biggest problem with hardware, mused LeCun, is that on the training side of things, it is a duopoly between Nvidia, for GPUs, and Google's Tensor Processing Unit (TPU), repeating a point he had made last year at the International Solid-State Circuits Conference.

Even more interesting than hardware for training, LeCun said, is hardware design for inference. "You now want to run on an augmented reality device, say, and you need a chip that consumes milliwatts of power and runs for an entire day on a battery." LeCun reiterated a statement made a year ago that Facebook is working on various internal hardware projects for AI, including for inference, but he declined to go into details.

Also: Facebook's Yann LeCun says 'internal activity' proceeds on AI chips

Today's neural networks are tiny, Hinton noted, with really big ones having perhaps just ten billion parameters. Progress on hardware might advance AI just by making much bigger nets with an order of magnitude more weights. "There are one trillion synapses in a cubic centimeter of the brain," he noted. "If there is such a thing as General AI, it would probably require one trillion synapses."

As for what "common sense" might look like in a machine, nobody really knows, Bengio maintained. Hinton complained people keep moving the goalposts, such as with natural language models. "We finally did it, and then they said it's not really understanding, and can you figure out the pronoun references in the Winograd Schema Challenge," a question-answering task used a computer language benchmark. "Now we are doing pretty well at that, and they want to find something else" to judge machine learning he said. "It's like trying to argue with a religious person, there's no way you can win."

But, one reporter asked, what's concerning to the public is not so much the lack of evidence of human understanding, but evidence that machines are operating in alien ways, such as the "adversarial examples." Hinton replied that adversarial examples show the behavior of classifiers is not quite right yet. "Although we are able to classify things correctly, the networks are doing it absolutely for the wrong reasons," he said. "Adversarial examples show us that machines are doing things in ways that are different from us."

LeCun pointed out animals can also be fooled just like machines. "You can design a test so it would be right for a human, but it wouldn't work for this other creature," he mused. Hinton concurred, observing "house cats have this same limitation."

Also: LeCun, Hinton, Bengio: AI conspirators awarded prestigious Turing prize

"You have a cat lying on a staircase, and if you bounce a soccer ball down the stairs toward a care, the cat will just sort of watch the ball bounce until it hits the cat in the face."

Another thing that could prove a giant advance for AI, all three agreed, is robotics. "We are at the beginning of a revolution," said Hinton. "It's going to be a big deal" to many applications such as vision. Rather than analyzing the entire contents of a static image or video frame, a robot creates a new "model of perception," he said.

"You're going to look somewhere, and then look somewhere else, so it now becomes a sequential process that involves acts of attention," he explained.

Hinton predicted last year's work by OpenAI in manipulating a Rubik's cube was a watershed moment for robotics, or, rather, an "AlphaGo moment," as he put it, referring to DeepMind's Go computer.

LeCun concurred, saying that Facebook is running AI projects not because Facebook has an extreme interest in robotics, per se, but because it is seen as an "important substrate for advances in AI research."

It wasn't all gee-whiz, the three scientists offered skepticism on some points. While most research in deep learning that matters is done out in the open, some companies boast of AI while keeping the details a secret.

"It's hidden because it's making it seem important," said Bengio, when in fact, a lot of work in the depths of companies may not be groundbreaking. "Sometimes companies make it look a lot more sophisticated than it is."

Bengio continued his role among the three of being much more outspoken on societal issues of AI, such as building ethical systems.

When LeCun was asked about the use of facial recognition algorithms, he noted technology can be used for good and bad purposes, and that a lot depends on the democratic institutions of society. But Bengio pushed back slightly, saying, "What Yann is saying is clearly true, but prominent scientists have a responsibility to speak out." LeCun mused that it's not the job of science to "decide for society," prompting Bengio to respond, "I'm not saying decide, I'm saying we should weigh in because governments in some countries are open to that involvement."

Hinton, who frequently punctuates things with a humorous aside, noted toward the end of the gathering his biggest mistake with respect to Nvidia. "I made a big mistake back in 2009 with Nvidia," he said. "In 2009, I told an audience of 1,000 grad students they should go and buy Nvidia GPUs to speed up their neural nets. I called Nvidia and said I just recommended your GPUs to 1,000 researchers, can you give me a free one, and they said, No.

"What I should have done, if I was really smart, was take all my savings and put it into Nvidia stock. The stock was at $20 then, now it's, like, $250."

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AI on steroids: Much bigger neural nets to come with new hardware, say Bengio, Hinton, and LeCun - ZDNet

AI has fallen from glorious summers into dismal winters before. The temptation to predict another such tumble recurs naturally. So that is the question the BBC posed to AI researchers: Are we on the cusp of an AI winter:

The 10s were arguably the hottest AI summer on record with tech giants repeatedly touting AIs abilities.

AI pioneer Yoshua Bengio, sometimes called one of the godfathers of AI, told the BBC that AIs abilities were somewhat overhyped in the 10s by certain companies with an interest in doing so.

There are signs, however, that the hype might be about to start cooling off.

I keep up with this kind of thing. The answer is: Yes, and no. AI did surge past milestones during the 2010s that it had not been expected to cross for many more years:

2011 IBMs Watson wins at Jeopardy! IBM Watson: The inside story of how the Jeopardy-winning supercomputer was born, and what it wants to do next (Tech Republic, September 9, 2013)

2012 Google unveils a deep learning systems that recognized images of cats

2015 Image recognition systems outperformed humans in the ImageNet challenge

2016 AlphaGo defeats world Go champion Lee Sedol: In Two Moves, AlphaGo and Lee Sedol Redefined the Future (Wired, March 16, 2016)

2018 Self-driving cars hit the road as Googles Waymo launched (a very limited) self-driving taxi service in Phoenix, Arizona

But other headlines during the period have been less heeded:

Despite High Hopes, Self-Driving Cars Are Way in the Future (2019)

The Next Hot Job: Pretending to Be a Robot (2019)

Boeings Sidelined Fuselage Robots: What Went Wrong? (2019)

Self-driving cars: Hype-filled decade ends on sobering note (2019)

Tesla driver killed in crash with Autopilot active, NHTSA investigating (2016)

Dont fall for these 3 myths about AI, machine learning (2018)

A Sobering Message About the Future at AIs Biggest Party (2019)

And so on.

So which is it? AI Winter or Robot Overlords? I suggest neither. And so do active researchers.

Gary Marcus, an AI researcher at New York University, said: By the end of the decade there was a growing realisation that current techniques can only carry us so far.

He thinks the industry needs some real innovation to go further.

There is a general feeling of plateau, said Verena Rieser, a professor in conversational AI at Edinburgh[s Heriot Watt University.

One AI researcher who wishes to remain anonymous said were entering a period where we are especially sceptical about AGI.

Recent AI developments, notably those lumped under the rubric of Deep Learning have advanced the state-of-the-art in machine learning. Lets not forget that prior efforts, such as the poorly named Expert Systems, had faded because, well, they werent expert at all. Deep Learning systems, as highly flexible pattern matchers, will endure.

What is not coming is the long-predicted AI Overlord, or anything that is even close to surpassing human intelligence. Like any other tool we build, AI has its place when it amplifies and augments our abilities.

Just as tractors and diggers have not led to legions of people who no longer use their arms, the latest advances in AI will not lead to human serfs cowering before beneath an all-intelligent machine. If anything, AI will require more from us, not less, because how we choose to use these tools will make an increasingly stark difference between benefit and ruin.

As Samin Winiger, a former AI research at Google says, What we called AI or machine learning during the past 10-20 years, will be seen as just yet another form of computation

Machines are tool in the toolbox, not a replacement for minds. An AI winter would only be coming if we forgot that.

Here are some of Brendan Dixons earlier musings on the concept of an AI Winter:

Just a light frost? Or an AI winter? Its nice to be right once in a whilecheck out the evidence for yourself

and

AI WinterIs Coming:Roughly every decade since the late 1960s has experienced a promising wave of AI that later crashed on real-world problems, leading to collapses in research funding.

Read more here:
So Is an AI Winter Really Coming This Time? - Walter Bradley Center for Natural and Artificial Intelligence

A team of researchers from Aarhus University in Denmark let DeepMinds AlphaZero algorithm loose on a few quantum computing optimization problems and, much to everyones surprise, the AI was able to solve the problems without any outside expert knowledge. Not bad for a machine learning paradigm designed to win at games like Chess and StarCraft.

Youve probably heard of DeepMind and its AI systems. The UK-based Google sister-company is responsible for both AlphaZero and AlphaGo, the systems that beat the worlds most skilled humans at the games of Chess and Go. In essence, what both systems do is try to figure out what the optimal next set of moves is. Where humans can only think so many moves ahead, the AI can look a bit further using optimized search and planning methods.

Related:DeepMinds AlphaZero AI is the new champion in chess, shogi, and Go

When the Aarhus team applied AlphaZeros optimization abilities to a trio of problems associated with optimizing quantum functions an open problem for the quantum computing world they learned that its ability to learn new parameters unsupervised transferred over from games to applications quite well.

Per the study:

AlphaZero employs a deep neural network in conjunction with deep lookahead in a guided tree search, which allows for predictive hidden-variable approximation of the quantum parameter landscape. To emphasize transferability, we apply and benchmark the algorithm on three classes of control problems using only a single common set of algorithmic hyperparameters.

The implications for AlphaZeros mastery over the quantum universe could be huge. Controlling a quantum computer requires an AI solution because operations at the quantum level quickly become incalculable by humans. The AI can find optimum paths between data clusters in order to emerge better solutions in tandem with computer processors. It works a lot like human heuristics, just scaled to the nth degree.

An example of this would be an algorithm that helps a quantum computer sort through near-infinite combinations of molecules to come up with chemical compounds that would be useful in the treatment of certain illnesses. The current paradigm would involve developing an algorithm that relies on human expertise and databases with previous findings to point it in the right direction.

But the kind of problems were looking at quantum computers to solve dont always have a good starting point. Some of these, optimization problems like the Traveling Salesman Problem, need an algorithm thats capable of figuring things out without the need for constant adjustment by developers.

DeepMinds algorithm and AI system may be the solution quantum computings been waiting for. The researchers effectively employ AlphaZero as a Tabula Rasa for quantum optimization: It doesnt necessarily need human expertise to find the optimum solution to a problem at the quantum computing level.

Before we start getting too concerned about unsupervised AI accessing quantum computers, its worth mentioning that so far AlphaZeros just solved a few problems in order to prove a concept. We know the algorithms can handle quantum optimization, now its time to figure out what we can do with it.

The researchers have already received interest from big tech and other academic institutions with queries related to collaborating on future research. Not for nothing, but DeepMinds sister-company Google has a little quantum computing program of its own. Were betting this isnt the last weve heard of AlphaZeros adventures in the quantum computing world.

Read next: Cyberpunk 2077 has been delayed to September (thank goodness)

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AlphaZero beat humans at Chess and StarCraft, now it's working with quantum computers - The Next Web