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Beth Kindig publishes a free newsletter on tech stocks at Beth.Technology and runs a premium research service for stock investors. She is a San Francisco-based technology analyst with more than a decade of experience in analyzing private and public technology companies.

Artificial intelligence is frequently discussed yet its too early to show real gains. AIs major headwind is the cost of the investment, which will skew returns in the short-term. When the turnaround occurs, however, companies who are making the investment can expect to be rewarded disproportionately with a wide performance gap. In a recent report,McKinsey predicts AI leaders will see up to double the cash flow.

We can see some evidence of this in Alphabets revenue segment, Other Bets, which includes many AI projectswith a loss of $3.35 billion in 2018. Of this,Deep Mind is responsible for $571 million in losses and owes its parent company $1.4 billion. The autonomous driving project, Waymo, had its valuationcut by 40% due to delays last September.

We see other companies taking on massive and expensive AI projects, such as Baidu, Facebook, Tesla, Alibaba, Microsoft and Amazon. Except for Tesla, these companies are flush with cash and can afford the transition costs and capital expenditures required for artificial intelligence.

Despite tech giants pouring cash into AI investments, most of the industries that stand to benefit are not in the tech industry, per se. This week, I attended Re-Works Deep Learning and AI Summit, where AI engineers and executives gathered for presentations and discussions about the projects theyre spearheading.

Here are a few ways that AI is slated to make an impact sooner rather than later:

The next decade will determine if humans or machines are better are making a medical diagnosis as more health care companies turn to AI for accuracy. One problem that Curai is working on, is how to train a model to know when it doesnt know, so a human can intervene to avoid the misclassification of unknown diseases. This approach is known as physician-in-the-loop.

Learning and Drug Discovery at Re:Work Deep Learning and AI Summit

Beth Kindig / Re:Work Deep Learning and AI Summit

United Health received 36 million calls in 2017 with 7.6 million calls transferred to a representative. The AI platform solution involves deep learning for a pre-check portal and claim queue, Automatic Speech Recognition (ASR) to translate audio to text, and Natural Language Processing (NLP) for unsupervised clustering, to generate new call variables and automate transfer calls.

Retail had a large presence at the conference with Wal-Mart Labs, Proctor and Gamble and Target presenting on ways they plan to make the retail experience more optimized. Perhaps these companies are being more careful to embrace technology and AI after the last decade ushered in many competitors who stole critical turf (i.e. Amazon).

Imagine a shopping experience where the carts are plentiful, cashiers are always open, and inventory is fully stocked. Rather than focus on replacing cashiers, Wal-Mart is more focused on inventory control. This is a different approach than competitor Amazon Go, designed to be cashier-less.

Privacy has been in the headlines lately as regulators and social media users begin to question what is a fair exchange for a free service. While the battle is nearing two years since Cambridge Analytica, other companies are creating AI recommendation engines so powerful that little information is needed about the person making the choice; their preference is enough to determine what to recommend next.

Netflix is a leader here with its recommendation engine for content. Pinterest also employs a complex recommendation engine to surface the best image for an individual out of the billions of images on Pinterests platform. This is done through the process of query understanding to candidate generation to ranking to blending to the final result. In laymans terms, this is how a discovery engine narrows down choices from billions to hundreds.

Over the next few years, we will become hands-free and will have better posture and fewer car accidents. Once AI-assistants are fully built out, our interaction with mobile devices may become the brunt of criticism from future generations. Many companies are working to own this space as the ecosystem lock-in and data produced by AI-assistants will be incredibly valuable expect a full-fledged battle between Amazon, Google, Facebook and Apple in this space.

Beth Kindig is a technology analyst who publishes weekly atbeth.technologyand runs a premium service for serious tech investors.

Beth predicted the biggest stock drop

Beth Kindig is a technology analyst who publishes weekly atbeth.technologyand runs a premium service for serious tech investors.

Beth predicted the biggest stock drop in history in Q2 2018 with Facebook's miss and the biggest IPO loss in history with Uber in Q2 2019. She invested in Roku from its IPO for a 1,000% gain and she is known for having a crystal ball on Twitter. Her experience comes from a decade of analyzing tech companies, tech products and startups resulting in over 700 articles and many enterprise-level analyst reports. She speaks frequently at tech conferences covering macro trends and has a tech podcast in the Top 40 for technology on iTunes and Spotify.She works in San Francisco and Silicon Valley as a data technology evangelist with over 7 years of experience in mobile and data. She has been published in many publications including VentureBeat, MediaPost, AdExchanger and the International Association of Privacy Professionals. She has written Quarterly Data Reports on technology since 2014.

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5 Soon-to-Be Trends in Artificial Intelligence And Deep Learning - Forbes

Graham Allison alerts us to artificial intelligence being the epicenter of todays superpower arms race.

Drawing heavily on Kai-Fu Lees basic thesis, Allison draws the battlelines: the United States vs. China, across the domains of human talent, big data, and government commitment.

Allison further points to the absence of controls, or even dialogue, on what AI means for strategic stability. With implied resignation, his article acknowledges the smashing of Pandoras Box, noting many AI advancements occur in the private sector beyond government scrutiny or control.

However, unlike the chilling and destructive promise of nuclear weapons, the threat posed by AI in popular imagination is amorphous, restricted to economic dislocation or sci-fi depictions of robotic apocalypse.

Absent from Allisons call to action is explaining the so what?why does the future hinge on AI dominance? After all, the few examples (mass surveillance, pilot HUDs, autonomous weapons) Allison does provide reference continued enhancements to the status quoincremental change, not paradigm shift.

As Allison notes, President Xi Jinping awoke to the power of AI after AlphaGo defeated the worlds number one Go human player, Lee Sedol. But why? What did Xi see in this computation that persuaded him to make AI the centerpiece of Chinese national endeavor?

The answer: AIs superhuman capacity to think.

To explain, lets begin with what I am not talking about. I do not mean so-called general AIthe broad-spectrum intelligence with self-directed goals acting independent of, or in spite of, preferences of human creators.

Eminent figures such as Elon Musk and Sam Harris warn of the coming of general AI. In particular, the so-called singularity, wherein AI evolves the ability to rewrite its own code. According to Musk and Harris, this will precipitate an exponential explosion in that AIs capability, realizing 10,000 IQ and beyond in a matter of mere hours. At such time, they argue, AI will become to us what we are to ants, with similar levels of regard.

I concur with Sam and Elon that the advent of artificial general superintelligence is highly probable, but this still requires transformative technological breakthroughs the circumstances for which are hard to predict. Accordingly, whether general AI is realized 30 or 200 years from now remains unknown, as is the nature of the intelligence created; such as if it is conscious or instinctual, innocent or a weapon.

When I discuss the AI arms race I mean the continued refinement of existing technology. Artificial intelligence that, while being a true intelligence in the sense of having the ability to self-learn, it has a single programmed goal constrained within a narrow set of rules and parameters (such as a game).

To demonstrate what President Xi saw in AI winning a strategy game, and why the global balance of power hinges on it, we need to talk briefly about games.

Artificial Intelligence and Games

There are two types of strategy games: games of complete information and games of incomplete information. A game of complete information is one in which every player can see all of the parameters and options of every other player.

Tic-Tac-Toe is a game of complete information. An average adult can solve this game with less than thirty minutes of practice. That is, adopt a strategy that no matter what your opponent does, you can correctly counter it to obtain a draw. If your opponent deviates from that same strategy, you can exploit them and win.

Conversely, a basic game of uncertainty is Rock, Scissors, Paper. Upon learning the rules, all players immediately know the optimal strategy. If your opponent throws Rock, you want to throw Paper. If they throw Paper, you want to throw Scissors, and so on.

Unfortunately, you do not know ahead of time what your opponent is going to do. Being aware of this, what is the correct strategy?

The unexploitable strategy is to throw Rock 33 percent of the time, Scissors 33 percent of the time, and Paper 33 percent of the time, each option being chosen randomly to avoid observable patterns or bias.

This unexploitable strategy means that, no matter what approach your opponent adopts, they won't be able to gain an edge against you.

But lets imagine your opponent throws Rock 100 percent of the time. How does your randomized strategy stack up? 33 percent of the time you'll tie (Rock), 33 percent of the time you'll win (Paper), and 33 percent of the time you'll lose (Scissors)the total expected value of your strategy against theirs is 0.

Is this your optimal strategy? No. If your opponent is throwing Rock 100 percent of the time, you should be exploiting your opponent by throwing Paper.

Naturally, if your opponent is paying attention they, in turn, will adjust to start throwing Scissors. You and your opponent then go through a series of exploits and counter-exploits until you both gradually drift toward an unexploitable equilibrium.

With me so far? Good. Let's talk about computing and games.

As stated, nearly any human can solve Tic-Tac-Toe, and computers solved checkers many years ago. However more complex games such as Chess, Go, and No-limit Texas Holdem poker have not been solved.

Despite all being mind-bogglingly complex, of the three chess is simplest. In 1997, reigning world champion Garry Kasparov was soundly beaten by the supercomputer Deep Blue. Today, anyone reading this has access to a chess computer on their phone that could trounce any human player.

Meanwhile, the eastern game of Go eluded programmers. Go has many orders of magnitude more combinations than chess. Until recently, humans beat computers by being far more efficient in selecting moveswe don't spend our time trying to calculate every possible option twenty-five moves deep. Instead, we intuitively narrow our decisionmaking to a few good choices and assess those.

Moreover, unlike traditional computers, people are able to think in non-linear abstraction. Humans can, for example, imagine a future state during the late stages of the game beyond which a computer could possibly calculate. We are not constrained by a forward-looking linear progression. Humans can wonderfully imagine a future endpoint, and work backwards from there to formulate a plan.

Many previously believed that this combination of factorsnear-infinite combinations and the human ability to think abstractlymeant that go would forever remain beyond the reach of the computer.

Then in 2016 something unprecedented happened. The AI system, AlphaGo, defeated the reigning world champion go player Lee Sedol 4-1.

But that was nothing: two years later, a new AI system, AlphaZero, was pitched against AlphaGo.

Unlike its predecessor which contained significant databases of go theory, all AlphaZero knew was the rules, from which it played itself continuously over forty days.

After this period of self-learning, AlphaZero annihilated AlphaGo, not 4-1, but 100-0.

In forty days AlphaZero had superseded 2,500 years of total human accumulated knowledge and even invented a range of strategies that had never been discovered before in history.

Meanwhile, chess computers are now a whole new frontier of competition, with programmers pitting their systems against one another to win digital titles. At the time of writing the world's best chess engine is a program known as Stockfish, able to smash any human Grandmaster easily. In December 2017 Stockfish was pitted against AlphaZero.

Again, AlphaZero only knew the rules. AlphaZero taught itself to play chess over a period of nine hours. The result over 100 games? AlphaZero twenty-eight wins, zero losses, seventy-two draws.

Not only can artificial intelligence crush human players, it also obliterates the best computer programs that humans can design.

Artificial Intelligence and Abstraction

Most chess computers play a purely mathematical strategy in a game yet to be solved. They are raw calculators and look like it too. AlphaZero, at least in style, appears to play every bit like a human. It makes long-term positional plays as if it can visualize the board; spectacular piece sacrifices that no computer could ever possibly pull off, and exploitative exchanges that would make a computer, if it were able, cringe with complexity. In short, AlphaZero is a genuine intelligence. Not self-aware, and constrained by a sandboxed reality, but real.

Despite differences in complexity there is one limitation that chess and go both share they're games of complete information.

Enter No-limit Texas Holdem (hereon, Poker). This is the ultimate game of uncertainty and incomplete information. In poker, you know what your hole cards are, the stack sizes for each player, and the community cards that have so far come out on the board. However, you don't know your opponent's cards, whether they will bet or raise or how much, or what cards are coming out on later streets of betting.

Poker is arguably the most complex game in the world, combining mathematics, strategy, timing, psychology, and luck. Unlike Chess or Go, Pokers possibilities are truly infinite and across multiple players simultaneously. The idea that a computer could beat top Poker professionals seems risible.

Except that it has already happened. In 2017, the AI system Libratus comprehensively beat the best Head's-up (two-player) poker players in the world.

And now, just months ago, another AI system Pluribus achieved the unthinkableit crushed super high stakes poker games against multiple top professionals simultaneously, doing so at a win-rate of five big blinds per hour. For perspective, the difference in skill level between the best English Premier League soccer team and the worst would not be that much.

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Explained: The Artificial Intelligence Race is an Arms Race - The National Interest Online

NASA/JPL-Caltech/UCLA/MPS/DLR/IDA

Ever since the Dawn spacecraft picked up images of what look to be a vast network of bright spots in the Occator crater on Ceresa dwarf planet in the asteroid beltthere's been conjecture over whether the whiteish spots are made up of ice, or some kind of volcanic salt deposits. Meanwhile, another controversy has been brewing over them: What exactly are those shapes seen in the bright spots, called Vinalia Faculae? Are they squares or triangles? Did extraterrestrials create them?

Because the strange patterns are so strikingly geometric, researchers from the University of Cadiz in Spain have taken a closer look at the bright spots to figure out whether humans and machines look at planetary images differently. The overall goal was to figure out if artificial intelligence can help us discover and make sense of technosignatures, or potentially detectable signals from distant, advanced civilizations, according to NASA.

"One of the potential applications of artificial intelligence is not only to assist in big data analysis but to help to discern possible artificiality or oddities in patterns of either radio signals, megastructures or techno-signatures in general," the authors wrote in a new paper published in the scientific journal Acta Astronautica.

To figure out what people thought they saw in the images of Occator, study author Gabriel G. De la Torre, a neuropsychologist from the University of Cadiz in Spain, brought together 163 volunteers who had no prior astronomy training. Overwhelmingly, these people identified a square shape in the crater's bright spots.

Then, the same was done with an artificial vision system trained with convolutional neural networks, which are mostly used in image recognition. Training data for the neural net included thousands of images of both squares and triangles so the system could identify those shapes.

NASA/JPL-Caltech/UCLA/MPS/DLR/IDA/PSI

Strangely, the neural net saw the same square the people noticed, but also identified a triangle, as shown in the image above. It appears the square is inside a larger triangle. After the people in the study were faced with this new triangular option, the percentage of them who claimed to have seen a triangle skyrocketed.

It's just one example of how our minds can be easily tricked when faced with a false positive. If we're told a system identified a given blip, we're more likely to blindly believe and truly think we saw the same blip due to our own tendency toward confirmation bias, or interpreting information in a particular way to fit a pre-existing belief.

That makes AI potentially dangerous in the search for far-away extraterrestrial life. False positives can confuse researchers, compromising its own usefulness in detecting technosignatures. De la Torre points out in the paper that what this all adds up to is actually a commonality between humans and AI systems: We both struggle with implicit bias.

So, if not artificial structures, what exactly are those funny shapes in the Ceres bright spot images? The quick answer is "we don't know." But De la Torre has an idea: It's "probably just a play of light and shadow."

Read the rest here:
AI Will Probably Trick Us Into Thinking We Found Aliens - Popular Mechanics

One click, your mortgage process begins. Another click, that mortgage loan is pre-approved.Five minutes pass and you are ready to buy a home.

The digital application process for single-family mortgages has flourished with new technology, new companies entering the space and new capabilities that, even just 10 years ago, we wouldnt have thought possible.

Borrowers can sign closing papers on a new home remotely so that they dont have to miss hours of work. Travelers can close on their home from the other side of the world. The credit invisible, or those with no credit score, can learn more about their financial situation and what they can do to prepare to buy a home after a quick and painless application process.

But if you fast forward just a few weeks, to when a borrower is getting settled into their new home, the experience changes dramatically. Their mortgage loan is sold and their servicer steps in to introduce themselves. The smooth digital process is suddenly transformed into a rough, paper-heavy and confusing process.

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Coming soon: The promise of artificial intelligence in servicing - HousingWire

In 2018, Google unveiled Duplex, an artificial intelligence-powered assistant that sounds eerily human-like, complete with umms and ahs that are designed to make the conversation more natural. The demo had Duplex call a salon to schedule a haircut and then call a restaurant to make a reservation.

As Googles CEO Sundar Pichai demonstrated, the system at Googles I/O (input/output) developer conference, the crowd cheered, hailing the technological achievement. Indeed, this represented a big leap toward developing AI voice assistants that can pass the Turing Test, which requires machines to be able to hold conversations while being completely indistinguishable from humans.

But not everyone was so enthusiastic. Some technology commentators saw it as a form of deception by design. In a 2018 tweet, prominent University of North Carolina techno-sociologist Zeynep Tufekici described the system as horrifying, and wrote: Silicon Valley is ethically lost, rudderless, and has not learned a thing.

Responding to public pressure, a Google spokeswoman said in a statement, We are designing this feature with disclosure built-in, and well make sure the system is appropriately identified.

But what if knowing that we are interacting with a bot made for a worse human experience? Suppose you are interacting with a customer service agent that you know is just a computer program. Might you give yourself a little more license to use abusive language or to lob insults? After all, you are not going to hurt any real human beings. As satisfying as this might be, could this shift in your behavior lead to longer and less efficient customer service calls and a worse overall experience for you?

To explore these questions, we ran studies in which participants played a cooperation game with either a human associate or a bot that used AI to adapt its behavior to maximize its payoffs. This game was designed to capture situations in which each of the interacting parties can either act selfishly in an attempt to exploit the other, or act cooperatively in an attempt to attain a mutually beneficial outcome.

In some instances, participants were told who they were interacting with: a human or a bot. In others, we gave false information about the associates identity. Some were told they were interacting with a bot when they were actually interacting with a human, and others were told they were interacting with a human, when in fact it was a bot.

The results showed that bots posing as humans were very efficient at persuading the partner to cooperate in the game. In fact, these bots were better at eliciting cooperation with humans than other humans were. When the bots true nature was revealed, however, cooperation rates dropped significantly, and the bots superiority was negated.

In fact, among all conditions we studied, the best outcome was achieved when people interacted with bots but were told they were interacting with humans. This is precisely the situation that outraged people over the Google Duplex demo and that caused Google to back off and indicate that they will disclose the nonhuman nature of the system.

As AI systems continue to approach or exceed human-level performance in various tasks, bots will be increasingly capable of passing as humans. In the near future, we will interact with bots on the phone, social media, or even video, in a variety of contexts, from business to government to entertainment and they will be indistinguishable from their human counterparts.

Our research reveals that while the much-touted algorithmic transparency is important, it may sometimes come at a cost. So now we must ask ourselves: Should we allow companies to deceive us into thinking bots are human if this makes us happier customers or more polite, cooperative people? Or does interacting with a machine we believe is a human violate something sacred, like human dignity? What is important to us: transparency or efficiency? And in what context might we prefer one or the other? Although there is broad consensus that machines should be transparent about how they make decisions, it is less clear whether they should be transparent about who they are.

Science, including our own experiment, cannot answer this question, since it is a question about what we value most transparency or efficiency. Maybe we can have both and as humans learn to work cooperatively with machines. But until we do, society needs to recognize and grapple with the ethics and trade-offs.

Talal Rahwan is an associate professor at New York University Abu Dhabi. Jacob Crandall is an associate professor at Brigham Young University. Fatimah Ishowo-Oloko is a PhD graduate from Khalifa University. Iyad Rahwan is an associate professor at MIT and director of the Max Planck Institute for Human Development.

Continued here:
Ethics, efficiency, and artificial intelligence - The Boston Globe