Monthly Archives: February 2017

Science and tech

DeepMind's AI recently acted aggressively when threatened in a computer game.

Click through to see the top science and tech predictions for 2017

DeepMind's AI recently acted aggressively when threatened in a computer game.

Click through to see the top science and tech predictions for 2017

LIST: The biggest science and tech predictions for 2017

The first baby with three parents will be born

A new fertility technique allows doctors to replace defective DNA found within a mother's egg with the DNA from another female donor. The result is a baby born with the DNA of two mothers. The first three-parent baby may potentially be born around Christmas of 2017.

Source: The Telegraph

LIST: The biggest science and tech predictions for 2017

The first baby with three parents will be born

A new fertility technique allows doctors to replace defective DNA found within a mother's egg with the DNA

Scientists will discover the truth behind "dark matter"

Dark matter, a mysterious type of matter that makes up a little more than a quarter of the universe, is several experiments away from being detected.Dr. Katherine Freese, an expert in the field of dark matter, says 2017 may be the year "the 80-year-old dark matter puzzle will finally be solved."

Source: NBC

Scientists will discover the truth behind "dark matter"

Dark matter, a mysterious type of matter that makes up a little more than a quarter of the universe, is several experiments away from being

The first "artificial pancreas" for people withtype 1 diabetes will hit the market

The "MiniMed 670G," an FDA-approved artificial pancreas, will monitor blood sugar and deliver insulin doses. It is set to be available by Spring 2017.

Source: CBS News

The first "artificial pancreas" for people withtype 1 diabetes will hit the market

The "MiniMed 670G," an FDA-approved artificial pancreas, will monitor blood sugar and deliver insulin doses. It is set to be

Genetically modified mosquitoes might be released to fight Zika in the U.S.

A company that creates genetically modified mosquitoes that have their offspring die when they mate with wild female mosquitos, may begin trials in Florida in 2017.

Source: NPR

Genetically modified mosquitoes might be released to fight Zika in the U.S.

A company that creates genetically modified mosquitoes that have their offspring die when they mate with wild female mosquitos, may

Costumer service will depend on social media more

"Social messaging channels such as Facebook Messenger and Twitter Direct Message are becoming increasingly important tools for brand engagement and customer service resolution. Big brands are already seeing a major shift from public posts to private messages."

Source: Inc

Costumer service will depend on social media more

"Social messaging channels such as Facebook Messenger and Twitter Direct Message are becoming increasingly important tools for brand engagement and customer

The first "human head transplant" may occur

Sergio Canavero, an Italian neuroscientist, is preparing to perform the first human head transplant. The surgery is slated for 2017.

Source: CBS News

The first "human head transplant" may occur

Sergio Canavero, an Italian neuroscientist, is preparing to perform the first human head transplant. The surgery is slated for 2017.

Source: CBS News

A new space race

Buzz Aldrin, the second human on the moon, told NBC and Americans to "get ready for intense competition in the development of human spaceflight systems." He said the space race will lead to "technical and business innovations we don't yet appreciate or understand."

Source: NBC

A new space race

Buzz Aldrin, the second human on the moon, told NBC and Americans to "get ready for intense competition in the development of human spaceflight systems." He said the space race will lead to

Robot chefs will cook our food

Moley Robotics, a company that is building a robot chef capable of cooking 2,000 recipes, will begin selling in early 2017.

Source: Time

Robot chefs will cook our food

Moley Robotics, a company that is building a robot chef capable of cooking 2,000 recipes, will begin selling in early 2017.

Source: Time

For the first time in a century, the U.S. will experience a "total solar eclipse."

Source: Wall Street Journal

For the first time in a century, the U.S. will experience a "total solar eclipse."

Source: Wall Street Journal

The Cassini spacecraft's 20-year mission will come to an end

Since arriving at Saturn in 2004, Cassini has provided scientists with valuable data and images. NASA said the Cassini mission will end on September 15, 2017, when the spacecraft plunges into Saturn's atmosphere to burn.

Source: NASA

The Cassini spacecraft's 20-year mission will come to an end

Since arriving at Saturn in 2004, Cassini has provided scientists with valuable data and images. NASA said the Cassini mission will end on September

2017 will be less hot than 2016

While 2017 is still expected to be one of the hottest years on record because of climate change, it won't be as hot as 2016 due to the absence ofEl Nio and the warming conditions it creates. Forecasters predict a 1.13F drop in average temperatures.

Source: Climatecentral.org

2017 will be less hot than 2016

While 2017 is still expected to be one of the hottest years on record because of climate change, it won't be as hot as 2016 due to the absence ofEl Nio and the warming

Hackers will useartificial intelligence

James R. Clapper, the director of National Intelligence, said artificial intelligence will make life easier for everyone, even hackers.

Source: New York Times

Hackers will useartificial intelligence

James R. Clapper, the director of National Intelligence, said artificial intelligence will make life easier for everyone, even hackers.

Source: New York Times

The first HIV vaccine

"PRO 140," a drug currently undergoing trials, will have "expected commercialization in 2017.

Source: HIVequal.org

The first HIV vaccine

"PRO 140," a drug currently undergoing trials, will have "expected commercialization in 2017.

Source: HIVequal.org

More laptops will be able to double as tablets

"Its becoming increasingly difficult to innovate on a traditional clamshell laptop design. Consequently, PC makers are putting most of their attention on innovating around what the industry calls 2-in-1s, which feature a tablet-style design with an attachable keyboard."

Source: Time

More laptops will be able to double as tablets

"Its becoming increasingly difficult to innovate on a traditional clamshell laptop design. Consequently, PC makers are putting most of their attention on

China's lunar mission will bring back moon samples for the first time in 40 years

China has scheduled an unmanned moon sample-return mission, known as Chang'e 5, for 2017.

Source: Space.com

China's lunar mission will bring back moon samples for the first time in 40 years

China has scheduled an unmanned moon sample-return mission, known as Chang'e 5, for 2017.

Source: Space.com

Investments into artificial intelligence (AI) start ups will explode, but it might be a bust

"[Venture capitalist] will swarm startups in these spaces like sharks smelling chum in the water... Most of these startups will crash and burn without ever turning a profit. That said, a select few will drive truly deep innovation, and in doing so, reshape the world."

Source:Inc

Investments into artificial intelligence (AI) start ups will explode, but it might be a bust

"[Venture capitalist] will swarm startups in these spaces like sharks smelling chum in the water... Most of these

The first (real) images of the Milk Way's super-massive black hole

A network of nine telescopes around the globe are adding the finishing touches to their project: In early 2017, the telescopes will snap the first images ofSagittarius A*, the black hole at the center of the Milky Way.

Source: BBC

The first (real) images of the Milk Way's super-massive black hole

A network of nine telescopes around the globe are adding the finishing touches to their project: In early 2017, the telescopes will snap the

Terrifyingly, Google's Artificial Intelligence acts aggressive when cornered

Being a sore loser is not an admired quality; especially when it's a sophisticated piece of artificial intelligence that's lashing out.

Researchers at DeepMind, Google's artificial intelligence lab, recently performed a number of tests by having its most complex AI play a series of a games with a version of itself.

In the first game, two AI agents, one red and one blue, scramble to see who can collect the most apples, or green squares.

Link:

Terrifyingly, Google's Artificial Intelligence acts aggressive when cornered - Chron.com

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Whenever a hot new field starts to take off, youll inevitably hear sighs of regret by the many who wish theyd gotten into it when they had the chance. The billion-dollar question is, why didnt they? The answer is, they chose not to. Thats what separates successful people from the pack: the choices they make.

Theres currently a talent war going on in the deep learning space. Web service leaders Amazon, Google and Microsoft are scooping up talent and buying startups left and right in a race for facial and speech recognition technology used in cloud-based searches and other red-hot machine learning applications.

Last week, Ford invested $1 billion to become majority shareholder of Argo AI, a self-driving car startup. Microsoft bought natural- language research lab Maluuba in January. Last summer, Intel paid more than $400 million to acquire 48-person AI startup Nervana and Apple bought Turi. Salesforce acquired MegaMind in April. And so on.

Related:The Growth ofArtificial Intelligencein Ecommerce (Infographic)

The thing is, the entrepreneurs involved in those ventures didnt just wake up one day and opportunistically decide to do something thatll make them a fortune. Some were pioneers in the field. Others took big risks and even bigger leaps of faith over long periods of time to get to where they are today.

Argo CEO Bryan Selesky led hardware development for Googles self-driving car project and hails from Carnegie Mellons famed National Robotics Engineering Center. Naveen Rao was an engineer for a decade before the AI lightbulb went off in his head. He went back to school, earneda PhD in neuroscience, themcofounded Nervana.

The point is, none of these people knew the field would take off. Maybe they thought it might, but they certainly did not know in advance. Thats not why they got into it. They got into it because that was the path that felt right to them and only them.

Im always telling young up-and-comers to take their time finding the right career that captures their imagination --the one thing that gets them excited, sparks their creativity, and makes them happy to do, day in, day out --and focus on being the best at it. And Im always hearing dumb excuses about why they cant, wont or shouldnt do that.

They read a book or an article somewhere by someone who said they should build their personal brand, fake it til they make it, join the growing hoard of self-employed gig workers, or start lots of little online businesses and hope that it all adds up to something someday.

Thats all nonsense that will get you nowhere, except maybe living hand-to-mouth and further in debt.

Related:How This Entrepreneur Kept His Day Job While Starting a Business

The spoils go to those who follow their passion, take risksand focus only on doing what they do best. That may not apply to everyone, but if you dont want to end up looking longingly at success stories and wondering why youre not among them, then it applies to you.

Nobody ever knows in advance if what they do for a living will pay off in the long run. Nobody has a crystal ball, and everybody gets just one life to live. There are no do-overs. So career decisions always come down to basic fundamentals thathavent changed in ages:

First, understand that everything you do is a choice made of your own free will. Make it wisely. Make it because it makes sense and feels right to you, not because someone you dont know from Adam said you should do it. After all, youre the one who is going to have to live with the consequences. Own it.

Second, the only way to make good choices is to have good choices. That means getting out in the world, working hard, and learning from those with experience doing what you aspire to do. The more exposure you have to smart people and new opportunities, the more experience you gain, the better your available choices will become.

Related:HasArtificial IntelligenceArrived At The Sales Function Yet?

Lastly, remember that you are human. The rules do apply to you. There are laws of physics, biologyand economics that no amount of wishful thinking or positive psychology can overcome. By all means, shoot for the stars, but try to keep at least one foot planted on the ground at all times.

Those who got into AI arent any smarter, luckier, or more privileged than you or me. All they did was stay true to the path that felt right to them. Simple as that.

Steve Tobakis a management consultant, columnist, former senior executive, and author ofReal Leaders Dont Follow: Being Extraordinary in the Age of the Entrepreneur(Entrepreneur Press, October 2015).Tobak runs...

Read more:

How Artificial Intelligence Startups Struck Gold - Entrepreneur

Elon Musk is saying outlandish things again. Several months ago, the Tesla and SpaceX CEO said that chances are we are all living in a simulation. Thankfully, other writers have contested this in a kinder manner than I would have (the words I have for Musks theory aresomething along the lines of utter nonsense and logically self-defeating, but I digress).

Well, now Musk thinks that humans must merge with machines, or else become defunct from the threat of advanced artificial intelligence. I guess he no longer thinks we live in a computer simulation. Why worry about humans becoming defunct if we are all brains in a vat?

Having millions of dollars does not mean that one can construct logically coherent chains of thought.

All that aside, I have several major issues with Musks assessment.

On an argumentative level Musks claims seem to paint artificial intelligence as some sort monster we have no control over. He talks about the threat of A.I. while ignoring that humans are the ones who create and control it, thus ignoring that we could easily stop working on it as it currently stands (as this Skynet-esque threat) if we are really so concerned about it displacing people.

Further, claims like Musks ignore the reality that no matter how advanced A.I. becomes, it is still artificial and reliant on programming put into it by human minds that are ontologically distinct from mere neurological matter and functions.

But really, the underlying presupposition of Musks confused plea for the merger of humans and machines is the biggest problem here. It implicitly assumes that humans are mere technology to be exploited for profit and material success. In this view humans are not persons, with an ultimate goal of flourishing, but mere biological machinery that need to be upgraded to a biomechanical level. When ones ultimate meaning has no transcendent anchor or reference point (e.g. God as the transcendent Source and Ground of reality), humans will inevitably be reduced down to mere technology. The bloodbath that is secularized 20th century bears stark witness to this.

Of course, Musk and those like him fundamentally misunderstand that mind is quite distinct from brain. True, the mental and the neurological are inextricably related. But to think that consciousness is derived or secreted from neurological matter is a fundamental confusion of categories, the product of an age that has forgotten to think deeply about the nature of reality and what persons not just human beings, but human persons really and truly are.

Artificial intelligence, no matter how complex, is not the same as human consciousness:

Computational models of the mind would make sense if what a computer actually does could be characterized as an elementary version of what the mind does, or at least as something remotely like thinking. In fact, though, there is not even a useful analogy to be drawn here. A computer does not even really compute. We compute, using it as a tool. We can set a program in motion to calculate the square root of pi, but the stream of digits that will appear on the screen will have mathematical content only because of our intentions, and because wenot the computerare running algorithms. The computer, in itself, as an object or a series of physical events, does not contain or produce any symbols at all; its operations are not determined by any semantic content but only by binary sequences that mean nothing in themselves. The visible figures that appear on the computers screen are only the electronic traces of sets of binary correlates, and they serve as symbols only when we represent them as such, and assign them intelligible significances. The computer could just as well be programmed so that it would respond to the request for the square root of pi with the result Rupert Bear; nor would it be wrong to do so, because an ensemble of merely material components and purely physical events can be neither wrong nor right about anythingin fact, it cannot be about anything at all. Software no more thinks than a minute hand knows the time or the printed word pelican knows what a pelican is.

David Bentley Hart The Experience of God: Being, Consciousness, Bliss p. 219

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Artificial Intelligence and The Confusion of Our Age - Patheos (blog)

Image from Wikimedia Commons

Artificial intelligence, machine learning, cognitive computing, deep learning, and related terms have become interchangeable jargon referring to AI. Although it's hard to believe, the level of marketing hype around AI has even surpassed digital transformation.

To break through the hype and nonsense, I asked the Chief Data Scientist of Dun and Bradstreet to explain AI in straightforward business terms. It's a complicated assignment, so I went to Anthony Scriffignano, one of the smartest, most accomplished data scientists I know. Anthony is a brilliant communicator, making him an ideal candidate to explain AI.

In the short video embedded above, Anthony gives a succinct introduction to AI for business people. Watch the video and enjoy un-hyped truth about an important topic.

How to Implement AI and Machine Learning

The next wave of IT innovation will be powered by artificial intelligence and machine learning. We look at the ways companies can take advantage of it and how to get started.

The conversation is part of the CXOTALK series, where you can watch the full-length, unedited discussion with Anthony Scriffignano and read a complete transcript.

If there's nothing else that our industry is good for, it's creating terms that people can use that have ambiguous meaning, and can be taken to mean almost anything in any situation. And this is certainly one of them. So, it's one of those things that you understand, but then when you try to define it, scholars will disagree on the exact definition. But, artificial intelligence collectively is a bunch of technologies that we run into. So, you'll hear "AI." You'll hear "machine learning." You'll hear "deep learning," [or] sometimes "deep belief." "Neuromorphic computing" is something that you might run into, or "neural networks;" "natural language processing;" "inference algorithms;" "recommendation engines." All of these fall into that category.

And some of the things that you might touch upon are autonomous systems bots. Sometimes, we will hear talk of... Well, Siri is probably the most obvious example that anybody runs into (or any of the other I won't try to name them all because I'll forget one), but things of that nature where you have these assistants that try to sort of mimic the behavior of a person. When you're on a website, and it says, "Click here to talk to Shelly!" or "Click here to talk to Doug!" You're not talking to a person; you're talking to a bot. So, those are examples of this.

Generally speaking, that's the broad brush. And then if you think about it as a computer scientist, you would say that these are systems processes that are designed to do any one of several things. One of them is to mimic human behavior. Another one is to mimic human thought process. Another is to "behave intelligently" you know, put that in quotes. Another is to "behave rationally," and that's a subject of a huge debate. Another one is to "behave ethically," and that's an even bigger debate. Those are some of the categories that these systems and processes fall into.

And then there are ways to categorize the actual algorithms. So, there are deterministic approaches; there are non-deterministic approaches; there are rules-based approaches. So, there are different ways you can look at this: you can look at it from the bottom up; the way it just ended; or regarding what you see and touch and experience.

They're not synonymous. So, cognitive computing is very different than machine learning, and I will call both of them a type of AI. Just to try and describe those three. So, I would say artificial intelligence is all of that stuff I just described. It's a collection of things designed to either mimic behavior, mimic thinking, behave intelligently, behave rationally, behave empathetically. Those are the systems and processes that are in the collection of soup that we call artificial intelligence.

Cognitive computing is primarily an IBM term. It's a phenomenal approach to curating massive amounts of information that can be ingested into what's called the cognitive stack. And then to be able to create connections among all of the ingested material, so that the user can discover a particular problem, or a particular question can be explored that hasn't been anticipated.

Machine learning is almost the opposite of that. Where you have a goal function, you have something very specific that you try and define in the data. And, the machine learning will look at lots of disparate data, and try to create proximity to this goal function basically try to find what you told it to look for. Typically, you do that by either training the system, or by watching it behave, and turning knobs and buttons, so there's unsupervised, supervised learning. And that's very, very different than cognitive computing.

So, a model is a method of looking at a set of data in the past, or a set of data that's already been collected, and describing it in a mathematical way. And we have techniques based on regression, where we continue to refine that model until it behaves within a certain performance. It predicts the outcome that we intend it to predict, in retrospect. And then, assuming that we can extrapolate from the frame we're into the future, which is a big assumption, we can use that model to try to predict what happens going forward mathematically.

The most obvious example of this that we have right now is the elections, right? So we look at the polling data. We look at the phase of the moon. We look at the shoe sizes. Whatever we decide to look at, we say, "This is what's going to happen." And then, something happens that maybe the model didn't predict.

So, now we get into AI. The way some systems work, not all, is they say: "Show me something that looks like what you're looking for, and then I'll go find lots of other things that look just like it. So train me. Give me a webpage, and tell me on that web page which things you find to be interesting. I'll find a whole bunch of other web pages that looks like that. Give me a set of signals that you consider to be a danger, and then when I see those signals, I'll tell you that something dangerous is happening." That's what we call "training."

Sure. So imagine that I gave a whole bunch of people, and the gold standard here is that they have to be similarly incentivized and similarly instructed, so I can't get, you know, five computer scientists and four interns ... You try to get people that more or less have either they're completely randomly dispersed, or they're all trying to do the same thing. There are two different ways to do it, right? And you show them lots and lots of pictures, right? You show them pictures of mountains, mixed in with pictures of camels, and pictures of things that are maybe almost mountains, like ice cream cones; and you let them tell you which ones are mountains. And then, the machine is watching and learning from people's behavior when they pick out mountains, to pick out mountains like people do. That's called a heuristic approach.

AI, Automation, and Tech Jobs

There are some things that machines are simply better at doing than humans, but humans still have plenty going for them. Here's a look at how the two are going to work in concert to deliver a more powerful future for IT, and the human race.

When we look at people, and we model their behavior by watching it, and then doing the same thing they did. That's a type of learning. That heuristic modeling is one of the ways that machine learning can work, not the only way.

There's a lot of easy ways to trick this. So, people's faces are a great example. When you look at people's faces, and we probably all know that there are techniques for modeling with certain points on a face, you know, the corners of the eyes. I don't want to get into any IP here, but there are certain places where you build angles between these certain places, and then those angles don't typically change much. And then you see mugshots with people with their eyes wide open, or with crazy expressions in their mouth. And those are people trying to confound those algorithms by distorting their face. It's why you're not supposed to smile in your passport picture. But, machine learning has gotten much better than that now. We have things like the Eigenface, and other techniques for modeling the rotation and distortion of the face and determining that it's the same thing.

So, these things get better and better and better over time. And sometimes, as people try to confound the training, we learn from that behavior as well. So, this thing all feeds into itself, and these things get better, and better, and better. And eventually, they approach the goal, if you will, of yes, it only finds mountains. It never misses a mountain, and it never gets confused by an ice cream cone.

The original way that this was done was through gamification or just image tagging. So, they either had people play a game, or they had people trying to help, saying, "This is a mountain," "This is not a mountain," "This is Mount Fuji," "This is Mount Kilimanjaro." So, they got a bunch of words. They got a bunch of people that use words to describe pictures (like Amazon Mechanical Turk).

Using those techniques, they just basically curated a bunch of words and said, "Alright, the word 'mountain' is often associated with there's a high correlation statistically between the use of the word 'mountain' and this image. Therefore, when people are looking for a mountain, give them this image. When they're looking for Mount Fuji, give them this image and not this image." And that was a trick of using human brains and using words. That's not the only way it works today. There are many more sophisticated ways today.

Please see the list of upcoming CXOTALK episodes. Thank you to my colleague, Lisbeth Shaw, for assistance with this post.

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No hype, just fact: Artificial intelligence in simple business terms - ZDNet