Category Archives: Artificial Intelligence

When you think of artificial intelligence, you may think of Lt. Commander Data or C-3PO, but this AI will actually be the spacecraft rather than on board.

Exploring space has some far-out challengesand this is after weve sent robots to Mars and all manner of probes and orbiters to other planets, including Venus and Saturn. Future missions will venture deeper and deeper into unexplored star systems and galaxies that have only been observed via telescope. This is easier dreamed than done. Too many unforeseen obstacles could cause a craft to glitch or break down hundreds of thousands of miles away, which is why scientists developing these future missions need to be paranoid.

Space scientists Steve Chien and Kiri Wagstaff of NASAs Jet Propulsion Laboratory suggest that programming probes with advanced artificial intelligence will largely eliminate the need for prompts from the home planet that would have increasing difficulty reaching out to them the further they ventured into space. Not to mention that probes on more daring missions will have to be able to think for themselves, because they even more of them will be required and they will probably not be able to receive any intervention from Earth. It gets creepier with the realization that the capacity to learn will need to be wired into their computerized brains to make them adaptable.

"The goal is for A.I. to be more like a smart assistant collaborating with the scientist and less like programming assembly code," said Chien, who collaborated with Wagstaff on an article recently published in the journal Science Robotics. "It allows scientists to focus on the 'thinking' thingsanalyzing and interpreting datawhile robotic explorers search out features of interest."

Autonomous probes should be able to function on a hypersensitive level that includes understanding and carrying out mission requirements, recognizing geological phenomena and identifying differences between what passes for normal planetary conditions (depending on the planet) and extreme space weather. They should also be able to reprioritize if they eye something spontaneous, like ocean plumes erupting on watery worlds similar to Enceladus. Advancing the science of AI enough may even make them able to use their findings for future studies. Not having infinite fuel means the robo-brains will also need to make the call on which regions are worth delving into the most.

AI is already being prototyped for the Mars 2020 rover and could someday make once-impossible endeavors, like a mission to Alpha Centauri, possible, but even the researchers themselves admit it still has to level up.

"For the foreseeable future, there's a strong role for high-level human direction," Wagstaff said. "But A.I. is an observational tool that allows us to study science that we couldn't get otherwise."

(via Phys.org)

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Space probes of the future will have artificial intelligence, and it's kind of creepy - SYFY WIRE (blog)

As the CEO of Silicon Valley Artificial Intelligence, Pete Kane has founded multiple startups such as Healthcare Minnesota and Startup Venture Loft, which led to his most recent collaborative creation Silicon Valley Artificial Intelligence. The community group uses machine learning (ML) and artificial intelligence (AI) to collaborate on research projects that can make landmark discoveries in science and healthcare. Silicon Valley AI will host the Genomics Hackathon fromFriday through Sunday at Google Launchpad in San Francisco. We spoke to Kane to get the skinny on what AI means for the future, and whether we should be afraid of the machines turning on us.

Why should people be excited about AI?

AI is exciting because were all exploring it at the same pace. Its possibilities have captured undivided attention of the world's smartest and most innovative people. Its exciting because were early on in this field. Everyone can get involved. Everyone can dream up ways to use machine intelligence.

What are the biggest benefits of AI now, and in the future?

I think of AI in terms of healthcare, medicine and life sciences research. Right now there are fantastic algorithms for imaging analysis like radiology and dermatology. In the future, I believe AI will play a leading role in areas like drug discovery, personalized medicine and cancer genomics.

Should we fear singularity?

No.The singularity question is a bit overhyped. I feel like we should focus on using AI to increase our understanding of medicine and biology.

What intentions did the original founders have for Silicon Valley Artificial Intelligence?

Our original intention was to build community in the SF Bay Area AI scene. We wanted to build sustainable non-profit organization, where people could learn from one another and make meaningful connections on a regular basis.

What was the first thing that got you interested in AI?

When I realized the AI scene wanted healthcare data, I was all in. The previous organization I started was a healthcare innovation community in Minnesota (Healthcare.mn), so I knew I could add a lot to the emerging AI scene here.

What response has the group received from the Silicon Valley community?

Strong! Weve have built wonderful relationships with researchers, students, and industry. The gatherings we host draw a serious, motivated crowd and I think weve built a great culture.

How does genomics play into AI and affect everyday people?

Very little at the moment. The cost and accessibility of high-resolution genomic sequencing excludes the general population. Moreover, it is still largely exploratory how AI/ML and Deep Learning is being applied to genomics, and the interpretability of those results.

What results could be a product of the Genomics Hackathon on June 23?

Participants will be analyzing drug treatment pathways, creating mutation ranking algorithms and simulating drug interventions. When 150 of the smartest people in AI, Genomics, Bioinformatics and Computer Science come together to hack on a rare cancer (NF2) genomics dataset, amazing things are going to happen. Stay tuned.

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Q&A: Pete Kane, CEO of Silicon Valley Artificial Intelligence - San Jose Inside (blog)

Artificial intelligence might seem more the preserve of computer nerds and tech giants than pharma companies. But according to Boehringer Ingelheim's global chief data scientist, Philipp Diesinger, "the entire industry is looking at data science and AI".

This increased focus on data could drastically change the way drugs are developed and paid for. For example, AI will be vital if outcomes-based healthcare is to be successfully implemented, pointed out Philips' chief innovation & strategy officer, Jeroen Tas, who also stressed that AI really signaled a new way of handling data.

He described AI as "the way you interpret data. You constantly stream the data and add that data to the body of knowledge," he told EP Vantage during the AI Summit in London in May. "That's not the case today, because it's all in the head of the doctor."

Boehringer's Mr. Diesinger believes that what is new is the "combination of AI, big data and new perceptions of these deep analytical methods", as well as an increasing capacity for data storage and processing.

While some might question whether this marks a real change from existing approaches, Mr. Diesinger believes that "there is a perception now for data-driven decision making in businesses, and that has not been around before". He pointed out how AI has transformed the financial industry "using theoretical physicists and mathematicians to optimise trading. We're doing the same now with regards to decision-making within [Boehringer]."

The German company has been active in AI for around two years, and is using data to reduce the cost of drug development and enable earlier go/no-go decisions on pipeline candidates. According to Mr. Diesinger, the group wants to evolve from a pharma to a holistic healthcare company, with the help of AI.

Meanwhile, Philips has been narrowing its focus from technology in general to medtech alone - and has gone big on connected devices and data processing.

Improving cancer care

Oncology is one area where pharma companies are already employing AI. Notably, Novartis (NYSE:NVS), which has also been involved in AI for two or three years, recently signed a deal with IBM Watson to explore the technology's use in breast cancer care.

The collaboration's aims include identifying better treatment sequences or predictors of response, Pascal Touchon, Novartis' global head of oncology strategy, told EP Vantage.

The project will analyse data from existing electronic health records using Watson's AI expertise. So what does Novartis bring to the table? "We understand what the key questions are and what to do with the answers," Mr. Touchon replied.

The scope is not limited to patients receiving Novartis drugs as the company is interested in breast cancer generally. Mr. Touchon expects initial findings in less than a year and, if it is successful, "we believe this collaboration could then be applied to other cancers".

Another application for AI that both Novartis and Watson are exploring is clinical trial matching. A study presented at the recent Asco meeting found that using the technology reduced the time required to screen patients for eligibility by 78%.

"If you're better at scanning patients, this could lead to faster trial enrollment [and] faster development of innovation," Mr. Touchon said.

At a stroke

As for Boehringer, Mr. Diesinger would only give one example of its AI projects: the Angels Initiative, a joint venture with the European Stroke Organisation that gathers anonymous time stamp data from hospitals to reveal patterns in stroke care and identify potential pinch points. This could lead to improvements aimed at speeding up stroke treatment, ultimately resulting in better outcomes for patients.

One change in practice involves identifying stroke patients in the ambulance and carrying out simple tests, so the stroke team is waiting at the hospital entrance. "That saves something like 10 minutes right away," Mr. Diesinger said.

Also looking for patterns is London-based BenevolentAI, which hopes its machine-based learning approach to processing academic research, clinical studies and other health-related data will help identify correlations in data that could lead to new drugs and significantly speed up the process of drug development.

The company has already signed a deal worth up to $800m to develop two Alzheimer's drugs for an undisclosed US pharma group. This is good progress, but Jackie Hunter, BenevolentAI's chief executive, believes most big pharma companies, if they are doing anything in AI, are dabbling. "We need critical mass," she said.

Ms. Hunter also believes that if big pharma continues to sit on the sidelines and not integrate AI into their mainstream activities it could find itself over taken by other industries. Speaking at the Prism Series conference in London earlier this month Ms. Hunter said: "It would not surprise me if one of the top 10 companies in healthcare in 10 years will be [Alphabet's] Google or Vodafone."

Hurdles

AI could come into its own in outcomes-based pricing, an increasing focus for cost-conscious healthcare systems. While several outcomes-based deals have been announced, the approach still faces barriers.

"You might ask, why is it not happening? One reason is that's not the way care is being reimbursed today," said Philips' Mr. Tas.

Current practice involves paying for discrete events: "Consultation, procedure, medication". In contrast, outcomes-based strategies rely on continuous care. "You continuously monitor and you intervene at the moment it's needed, so you need another way to reimburse it."

Mr. Tas concluded that outcomes-based pricing was "not going to happen overnight because it's such a big shift. But it's happening, and we see it everywhere."

With plenty of other companies clamoring to get into healthcare, including tech giants like IBM Watson and Alphabet, how will medtech and pharma groups compete in the AI space?

"We're at the point of care," Mr. Tas said. "It's not only that we have the devices; it's that we're on the floor. We're working with clinicians on the ground, and they get the insight into what's needed, which perhaps someone who's set back from that is not going to be able to gain."

Boehringer's Mr. Diesinger agreed: "IBM Watson has some nice cases where it is diagnosing patients better than doctors, but to make it to a highly regulated traditional market there's a long way to go. We're not a technology company obviously, but we already have all this regulatory burden and access to healthcare figured out."

There are still issues to be ironed out, including cybersecurity dangers, illustrated by the ransomware attack in May that hit the UK's NHS as well as a recent report by the US Health Care Industry Cybersecurity Task Force highlighting the challenges the industry faces.

In AI we trust?

Even if cybersecurity is assured, others in the industry believe that one of the biggest hurdles AI in healthcare will have to overcome is patient trust.

Josh Sutton of Sapientrazorfish, a digital and AI consultancy group, says the big problem for health-based AI is that patients often want the answers about their health explained.

"In certain industries, like advertising for example, people don't care how you came up with an answer. In healthcare people are passionately obsessed, justifiably so, with how a decision was made to diagnose someone with cancer or recommend they have heart surgery."

This desire for transparency around diagnosis could require AI companies to give details of the algorithms used in their technology, something they might be reluctant to consider - or even enabling the technology to provide direct explanations to patients.

Mr. Sutton believes that this will become more of a focus as AI becomes more prevalent in the industry and could be a limiting step for the global adoption of the approach as a standalone outside of the human-plus-machine construct many see for the industry in the short term.

"The full automation of work that is done in the industry today will take a significantly longer time than [in] other industries simply because of how critical it is we get it right, and our need, correctly in my opinion, to understand how the decisions get made and why they get made," he said.

Mr. Diesinger of Boehringer agrees that overall, the pharma sector is a "couple of years behind other industries" in terms of using AI. But he feels that that could soon begin to change, particularly if healthcare spending comes under more pressure, forcing the sector to become more streamlined.

He said: "Managers are now much more interested in these new technologies and much more open to trying new things."

Editor's Note: This article discusses one or more securities that do not trade on a major U.S. exchange. Please be aware of the risks associated with these stocks.

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Artificial Intelligence Is The Real Thing For Pharma And Medtech - Seeking Alpha

Senraj Soundar, CEO, ConnectLeader discusses how AI-driven software can eliminate a great deal of manual work, helping sales reps make decisions about how to approach prospects, personalize conversations, and most importantly, focus on the leads that deserve follow-up

In 1996, IBM introduced a supercomputer called Deep Blue to the world. Deep Blue challenged world chess champion Garry Kasparov to a series of chess matches and Deep Blue won. In 2011, IBM introduced Watson, another supercomputer, which beat two of Jeopardy's greatest champions. In 2016, Go Master Lee Sedol played a match against an ominous new challenger, a super computer from Google named, DeepMind, which won the match, making it the first time a computer had defeated a Go master.

Watson was the beginning of a new era in computing. Computing started with mechanical switches that counted things. It then moved to programmable systems. The computer was told what to do and it improved our productivity. Watson was the first computer built for big data, for extracting an understanding from massive amounts of data to help humans make better decisions. As it was given data and given outcomes, it learned. The more data it got, the smarter it got. And it never forgot. As Watsons father John Kelly, senior vice president of IBM said, We wanted to help humans that were basically in this cognitive overload because of information. We wanted to help them make better decisions.

Yet, Stephen Hawking, Bill Gates and Elon Musk have all raised concerns about the threat that artificial intelligence (AI) poses. According to Musk, advanced AI could be more dangerous than nukes, while Hawking suggested that it could lead to the end of humanity. But, humans have always been masters of technology--steam power, coal, electricity, and now computerization--and it seems every new technology comes with its scare. Every day now we hear stories about AI, data and robotics, about the jobs threatened in manufacturing, retail, transportation, even in the legal, medical, and high finance worlds. The research firm PwC found that nearly 4 out of 10 jobs in the United States could be vulnerable to replacement by robots in the next fifteen years. Ford plans to invest $1 billion into AI. The goal is to have a fully autonomous vehicle on the commercial market by 2021.

Jeff Bezos, the founder of Amazon, seems to have infiltrated every aspect of our lives, especially with his personal assistant, Alexa, that delivers us the news, weather, and the fastest way to get to work. Buoy Health has launched a digital symptom-checker designed to simulate a conversation with a real doctor. Scientists at MIT have developed a wearable wrist device that can read the emotions of a conversation. AI seems to be everywhere--the computer part of all we do.

According to computer scientist, inventor and futurist, Ray Kurzweil, AI outperforms humans because of several aspects unique to machines:

In the business world, with sales, its all about understanding behaviors and motivations. An AI-based system can use computing power to find the best prospects. The computer can use masses of market data which can be compared and matched with ideal customer profiles, saving the sales rep hours and hours of manual labor.

Todays best reps use predictive analytics, a form of AI that optimizes decision making around sales efforts. Salesforce and Microsoft have AI-driven tools and investment in AI startups is at an all-time high. This type of software uses techniques that gather customer and prospect data from multiple sources, run it through machine learning models to predict which leads are most likely to convert, and present the findings to a sales team, in the form of best prospects and accounts. AI-driven software can eliminate a great deal of manual work, helping sales reps make decisions about how to approach prospects, personalize conversations, and most importantly, focus on the leads that deserve follow-up.

Here are a few data points which can be taken into consideration by an AI-based platform: size of company; location; recent ventures; length of sales cycle; revenue; growth rate; financial health; recruitments; relocations; funding rounds; installed technologies; intent to buy; social media activity.

AI engines can provide sales reps with quality connects and conversations with qualified buyers from all that data residing in the companys CRM system. By sourcing and analyzing the data coming from different sales channels (emails, calls, social media), the AI algorithms can provide optimal personalized propositions for customers. And when propelled by calling tools, the best leads will be reach at the most optimal time.

And at this point, it may be best to remember the story of John Henry. Unlike those in the beginning of this article who lost their games to supercomputers and had to suffer embarrassment, when John Henry took on the steam shovel and lost, he died. So, . Let advanced AI technology help you stop wasting valuable time and energy, and help make you optimize your lead generation by making you win more sales.

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How Artificial Intelligence Makes Lead Generation Smarter - MarTech Advisor

The reckless rise of artificial intelligence is going to be much more disruptive for the London technology scene in the longer run than Britains departure from the EU, according to musician, entrepreneur and philanthropist will.i.am.

Speaking at an event celebrating his collaboration with Atom Bank, an app-based digital-only bank launched last year, the founding member of The Black Eyed Peas said that by 2030, Brexit will be an old school thought for the UKs rapidly evolving tech industry and AI will present a much more acute challenge.

At the moment no one really understands the things that we should care about, he said. We need to invest in AI in order to stay ahead.

Echoing similar remarks made by Chinese business magnate Alibaba chairman Jack Ma this week, will.i.am said that historically, technology and industrialisation have caused wars.

Technology today hasnt caused turmoil [yet] but we need to make sure it doesnt, he said. We need to work closer together to be inspiring and encouraging, and to protect the youth.

A time when we do everything on our phones from banking to screening our medical health and even voting in elections is just around the corner.

Multimillionaire will.i.am shot to fame in the early 2000s with hip-hop group The Black Eyed Peas. Hes since released several solo albums, collaborated with scores of artists, including Michael Jackson, Justin Bieber, Britney Spears and Lady Gaga. Hes broken into television, with talent show The Voice and has also dabbled in fashion.

Hes a founding shareholder of Beats Electronics, which makes high-end headphones, and an avid philanthropist through his foundation dedicated to providing education to underprivileged students. In the UK, his foundation collaborates with The Princes Trust.

Earlier this year, Durham-based Atom announced that will.i.am had been appointed as the banks first strategic board advisor.

As a consumer technology investor, Atom at the time said that the 42-year old, whose real name is William James Adams, would provide an external perspective on culture, philanthropy and technology.

At this weeks event, hosted in a Shoreditch hotel, Anthony Thomson, founder and chairman of Atom, elaborated on the perhaps not quite obvious partnership.

He said that he had pitched to the musician around two years ago after trying to determine who Atom would be if it were a person.

Hes a guy who has all the qualities were looking for [in the bank], Mr Thomson, who is also the founder and chairman of Metro Bank, said.

will.i.am told The Independent that he had chosen to work with Atom because of its progressive approach to banking and the way in which it strives to educate especially young people about saving, in a prescient manner.

When he got his first pay cheque after securing a record deal at 20 years old, he had no clue how to manage his finances. He said that he developed a habit of stashing the cheques he was receiving in the locked glove compartment of his car. That was my idea of saving.

A new age type of banking company, as will.i.am describes Atom, will ensure that young peopleespecially those from a deprived background who have little understanding of personal finance are given the opportunity to learn how to manage their savings. He said that he grew up in a poor neighbourhood in east Los Angeles and could relate to youngsters today trying to make living.

As a celebrity, he said, he can help Atom raise awareness and drive adoption and help the lender be to the larger, established banks what a true disruptor, like Uber is to the traditional taxi industry.

This is all about preparing for tomorrow, he said. No one wants to play catch up.

Atom, which received its banking licence in June 2015, currently offers several savings products and has entered the mortgage market by partnering with brokers. In March it raised 83m from major institutional investors, including Spains BBVA, veteran fund manager Neil Woodford and Toscafund. It said that it intends to launch further products this year.

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Brexit: will.i.am says artificial intelligence will be more disruptive to ... - The Independent

While some fear artificial intelligence may take jobs from humans, technology company SAP sees it as a way to potentially make better hires and increase diversity.

The German technology company, which employs more than 1,500 people in the Chicago area, is introducing a tool that will allow recruiters to use machine learning to sift through thousands of applications much faster.

SAP Resume Matching applies machine learning to the process of matching resumes and job descriptions, said DJ Paoni, SAPs Chicago-based Midwest managing director. SAP will roll the product out to its own recruiters this year and will also sell it to clients.

Paoni said the tool extracts information such as skills and experience from resumes and scores them against particular open positions. That can allow a recruiter to more easily whittle down a pool of a thousand to several dozen that are worth further consideration, he said.

It really allows recruiters to focus on the important part of that whole process, which is interacting with the candidates, as opposed to poring through resumes and trying to match job descriptions, Paoni said.

He said SAP plans to use this tool to help make better hires. Over the past eight years or so, the company has shifted to taking on more young, entry-level employees for the first time. As a result, its paying more attention to hiring and retention trends, such as the impact of employee well-being on productivity, diversity and inclusion, the use of part-time or supplemental workers and continuous feedback rather than annual reviews.

To make SAP a better workplace based on those trends, it needs to be quicker and better at finding the best hires, he said.

Automating the resume sorting process could also help remove bias from the hiring process, Paoni said, creating a more diverse candidate pool.

SAP also plans to use machine learning to score job descriptions themselves a process that could help identify unconscious bias in listings. For example, terms like rockstar or ninja may be more attractive to men than women. The company is developing a new machine learning tool that would detect this kind of language using sentiment analysis, then suggest alternatives.

Its similar to a grammar check that you might do on a document, Paoni said. The system will recommend alternative words for terms that might hint at a pattern of unconscious bias.

He said that will help SAP as it pushes for more diversity and inclusion. But Paoni said the machine learning tools are intended to speed up and supplement the recruiting process, not replace it.

If you rely too much on the technology, you lose that personal feel, he said. It's a delicate balance.

aelahi@chicagotribune.com Twitter @aminamania

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Can resume-reading software help companies make better hires? - Chicago Tribune

Much of the recent progress in AI research has been courtesy of an approach known as deep learning.

When most people think of artificial intelligence (AI) they think of HAL 9000 from "2001: A Space Odyssey," Data from "Star Trek," or more recently, the android Ava from "Ex Machina." But to a computer scientist that isn't what AI necessarily is, and the question "what is AI?" can be a complicated one.

One of the standard textbooks in the field, by University of California computer scientists Stuart Russell and Google's director of research, Peter Norvig, puts artificial intelligence in to four broad categories:

The differences between them can be subtle, notes Ernest Davis, a professor of computer science at New York University. AlphaGo, the computer program that beat a world champion at Go, acts rationally when it plays the game (it plays to win). But it doesn't necessarily think the way a human being does, though it engages in some of the same pattern-recognition tasks. Similarly, a machine that acts like a human doesn't necessarily bear much resemblance to people in the way it processes information.

Decades of research and speculative fiction have led to today's computerized assistants such as Apple's Siri.

Even IBM's Watson, which acted somewhat like a human when playing Jeopardy, wasn't using anything like the rational processes humans use.

Davis says he uses another definition, centered on what one wants a computer to do. "There are a number of cognitive tasks that people do easily often, indeed, with no conscious thought at all but that are extremely hard to program on computers. Archetypal examples are vision and natural language understanding. Artificial intelligence, as I define it, is the study of getting computers to carry out these tasks," he said.

Computer vision has made a lot of strides in the past decade cameras can now recognize faces in the frame and tell the user where they are. However, computers are still not that good at actually recognizing faces, and the way they do it is different from the way people do. A Google image search, for instance, just looks for images in which the pattern of pixels matches the reference image. More sophisticated face recognition systems look at the dimensions of the face to match them with images that might not be simple face-on photos. Humans process the information rather differently, and exactly how that process works is still something of an open question for neuroscientists and cognitive scientists.

Other tasks, though, are proving tougher. For example, Davis and NYU psychology professor Gary Marcus wrote in the Communications of the Association for Computing Machinery of "common sense" tasks that computers find very difficult. A robot serving drinks, for example, can be programmed to recognize a request for one, and even to manipulate a glass and pour one. But if a fly lands in the glass the computer still has a tough time deciding whether to pour the drink in and serve it (or not).

The issue is that much of "common sense" is very hard to model. Computer scientists have taken several approaches to get around that problem. IBM's Watson, for instance, was able to do so well on Jeopardy! because it had a huge database of knowledge to work with and a few rules to string words together to make questions and answers. Watson, though, would have a difficult time with a simple open-ended conversation.

Beyond tasks, though, is the issue of learning. Machines can learn, said Kathleen McKeown, a professor of computer science at Columbia University. "Machine learning is a kind of AI," she said.

Some machine learning works in a way similar to the way people do it, she noted. Google Translate, for example, uses a large corpus of text in a given language to translate to another language, a statistical process that doesn't involve looking for the "meaning" of words. Humans, she said, do something similar, in that we learn languages by seeing lots of examples.

That said, Google Translate doesn't always get it right, precisely because it doesn't seek meaning and can sometimes be fooled by synonyms or differing connotations.

One area that McKeown said is making rapid strides is summarizing texts; systems to do that are sometimes employed by law firms that have to go through a lot of it.

McKeown also thinks personal assistants is an area likely to move forward quickly. "I would look at the movie 'Her,'" she said. In that 2013 movie starring Joaquin Phoenix, a man falls in love with an operating system that has consciousness.

"I initially didn't want to go see it, I said that's totally ridiculous," McKeown said. "But I actually enjoyed it. People are building these conversational assistants, and trying to see how far can we get."

The upshot is AIs that can handle certain tasks well exist, as do AIs that look almost human because they have a large trove of data to work with. Computer scientists have been less successful coming up with an AI that can think the way we expect a human being to, or to act like a human in more than very limited situations.

"I don't think we're in a state that AI is so good that it will do things we hadn't imagined it was going to do," McKeown said.

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What Is Artificial Intelligence?

People often think of artificial intelligence as something akin to the being from the film "I, Robot" depicted here, but experts are divided on what the future actually holds.

It's a Saturday morning in June at the Royal Society in London. Computer scientists, public figures and reporters have gathered to witness or take part in a decades-old challenge. Some of the participants are flesh and blood; others are silicon and binary. Thirty human judges sit down at computer terminals, and begin chatting. The goal? To determine whether they're talking to a computer program or a real person.

The event, organized by the University of Reading, was a rendition of the so-called Turing test, developed 65 years ago by British mathematician and cryptographer Alan Turing as a way to assess whether a machine is capable of intelligent behavior indistinguishable from that of a human. The recently released film "The Imitation Game," about Turing's efforts to crack the German Enigma code during World War II, is a reference to the scientist's own name for his test.

In the London competition, one computerized conversation program, or chatbot, with the personality of a 13-year-old Ukrainian boy named Eugene Goostman, rose above and beyond the other contestants. It fooled 33 percent of the judges into thinking it was a human being. At the time, contest organizers and the media hailed the performance as an historic achievement, saying the chatbot was the first machine to "pass" the Turing test. [Infographic: History of Artificial Intelligence]

Decades of research and speculative fiction have led to today's computerized assistants such as Apple's Siri.

When people think of artificial intelligence (AI) the study of the design of intelligent systems and machines talking computers like Eugene Goostman often come to mind. But most AI researchers are focused less on producing clever conversationalists and more on developing intelligent systems that make people's lives easier from software that can recognize objects and animals, to digital assistants that cater to, and even anticipate, their owners' needs and desires.

But several prominent thinkers, including the famed physicist Stephen Hawking and billionaire entrepreneur Elon Musk, warn that the development of AI should be cause for concern.

Thinking machines

The notion of intelligent automata, as friend or foe,dates back to ancient times.

"The idea of intelligence existing in some form that's not human seems to have a deep hold in the human psyche," said Don Perlis, a computer scientist who studies artificial intelligence at the University of Maryland, College Park.

Reports of people worshipping mythological human likenesses and building humanoid automatons date back to the days of ancient Greece and Egypt, Perlis told Live Science. AI has also featured prominently in pop culture, from the sentient computer HAL 9000 in Stanley Kubrick's "2001: A Space Odyssey" to Arnold Schwarzenegger's robot character in "The Terminator" films. [A Brief History of Artificial Intelligence]

Since the field of AI was officially founded in the mid-1950s, people have been predicting the rise of conscious machines, Perlis said. Inventor and futurist Ray Kurzweil, recently hired to be a director of engineering at Google, refers to a point in time known as "the singularity," when machine intelligence exceeds human intelligence. Based on the exponential growth of technology according to Moore's Law (which states that computing processing power doubles approximately every two years), Kurzweil has predicted the singularity will occur by 2045.

But cycles of hype and disappointment the so-called "winters of AI" have characterized the history of artificial intelligence, as grandiose predictions failed to come to fruition. The University of Reading Turing test is just the latest example: Many scientists dismissed the Eugene Goostman performance as a parlor trick; they said the chatbot had gamed the system by assuming the persona of a teenager who spoke English as a foreign language. (In fact, many researchers now believe it's time to develop an updated Turing test.)

Nevertheless, a number of prominent science and technology experts have expressed worry that humanity is not doing enough to prepare for the rise of artificial general intelligence, if and when it does occur. Earlier this week, Hawking issued a dire warning about the threat of AI.

"The development of fullartificial intelligencecould spell the end of the human race," Hawking told the BBC, in response to a question about his new voice recognition system, which uses artificial intelligence to predict intended words. (Hawking has a form of the neurological disease amyotrophic lateral sclerosis, ALS or Lou Gehrig's disease, and communicates using specialized speech software.)

And Hawking isn't alone. Musk told an audience at MIT that AI is humanity's "biggest existential threat." He also once tweeted, "We need to be super careful with AI. Potentially more dangerous than nukes."

In March, Musk, Facebook CEO Mark Zuckerberg and actor Ashton Kutcher jointly invested $40 million in the company Vicarious FPC, which aims to create a working artificial brain. At the time, Musktold CNBCthat he'd like to "keep an eye on what's going on with artificial intelligence," adding, "I think there's potentially a dangerous outcome there."

Fears of AI turning into sinister killing machines, like Arnold Schwarzenegger's character from the "Terminator" films, are nothing new.

But despite the fears of high-profile technology leaders, the rise of conscious machines known as "strong AI" or "general artificial intelligence" is likely a long way off, many researchers argue.

"I don't see any reason to think that as machines become more intelligent which is not going to happen tomorrow they would want to destroy us or do harm," said Charlie Ortiz, head of AI at the Burlington, Massachusetts-based software company Nuance Communications."Lots of work needs to be done before computers are anywhere near that level," he said.

Machines with benefits

Artificial intelligence is a broad and active area of research, but it's no longer the sole province of academics; increasingly, companies are incorporating AI into their products.

And there's one name that keeps cropping up in the field: Google. From smartphone assistants to driverless cars, the Bay Area-based tech giant is gearing up to be a major player in the future of artificial intelligence.

Google has been a pioneer in the use of machine learning computer systems that can learn from data, as opposed to blindly following instructions. In particular, the company uses a set of machine-learning algorithms, collectively referred to as "deep learning," that allow a computer to do things such as recognize patterns from massive amounts of data.

For example, in June 2012, Google created a neural network of 16,000 computers that trained itself to recognize acatby looking at millions of cat images from YouTube videos, The New York Timesreported. (After all, what could be more uniquely human than watching cat videos?)

The project, called Google Brain, was led by Andrew Ng, an artificial intelligence researcher at Stanford University who is now the chief scientist for the Chinese search engine Baidu, which is sometimes referred to as "China's Google."

Today, deep learning is a part of many products at Google and at Baidu, including speech recognition, Web search and advertising, Ng told Live Science in an email.

Current computers can already complete many tasks typically performed by humans. But possessing humanlike intelligence remains a long way off, Ng said. "I think we're still very far from the singularity. This isn't a subject that most AI researchers are working toward."

Gary Marcus, a cognitive psychologist at NYU who has written extensively about AI, agreed. "I don't think we're anywhere near human intelligence [for machines]," Marcus told Live Science. In terms of simulating human thinking, "we are still in the piecemeal era."

Instead, companies like Google focus on making technology more helpful and intuitive. And nowhere is this more evident than in the smartphone market.

Artificial intelligence in your pocket

In the 2013 movie "Her," actor Joaquin Phoenix's character falls in love with his smartphone operating system, "Samantha," a computer-based personal assistant who becomes sentient. The film is obviously a product of Hollywood, but experts say that the movie gets at least one thing right: Technology will take on increasingly personal roles in people's daily lives, and will learn human habits and predict people's needs.

Anyone with an iPhone is probably familiar with Apple's digital assistant Siri, first introduced as a feature on the iPhone 4S in October 2011. Siri can answer simple questions, conduct Web searches and perform other basic functions. Microsoft's equivalent is Cortana, a digital assistant available on Windows phones. And Google has the Google app, available for Android phones or iPhones, which bills itself as providing "the information you want, when you need it."

For example, Google Now can show traffic information during your daily commute, or give you shopping list reminders while you're at the store. You can ask the app questions, such as "should I wear a sweater tomorrow?" and it will give you the weather forecast. And, perhaps a bit creepily, you can ask it to "show me all my photos of dogs" (or "cats," "sunsets" or a even a person's name), and the app will find photos that fit that description, even if you haven't labeled them as such.

Given how much personal data from users Google stores in the form of emails, search histories and cloud storage, the company's deep investments in artificial intelligence may seem disconcerting. For example, AI could make it easier for the company to deliver targeted advertising, which some users already find unpalatable. And AI-based image recognition software could make it harder for users to maintain anonymity online.

But the company, whose motto is "Don't be evil," claims it can address potential concerns about its work in AI by conducting research in the open and collaborating with other institutions, company spokesman Jason Freidenfelds told Live Science. In terms of privacy concerns, specifically, he said, "Google goes above and beyond to make sure your information is safe and secure," calling data security a "top priority."

While a phone that can learn your commute, answer your questions or recognize what a dog looks like may seem sophisticated, it still pales in comparison with a human being. In some areas, AI is no more advanced than a toddler. Yet, when asked, many AI researchers admit that the day when machines rival human intelligence will ultimately come. The question is, are people ready for it?

In the film "Transcendence," Johnny Depp's character uploads his mind to a computer, but it doesn't end well.

Taking AI seriously

In the 2014 film "Transcendence," actor Johnny Depp's character uploads his mind into a computer, but his hunger for power soon threatens the autonomy of his fellow humans. [Super-Intelligent Machines: 7 Robotic Futures]

Hollywood isn't known for its scientific accuracy, but the film's themes don't fall on deaf ears. In April, when "Trancendence" was released, Hawking and fellow physicist Frank Wilczek, cosmologist Max Tegmark and computer scientist Stuart Russell published an op-ed in The Huffington Post warning of the dangers of AI.

"It's tempting to dismiss the notion of highly intelligent machines as mere science fiction," Hawking and others wrote in the article."But this would be a mistake, and potentially our worst mistake ever."

Undoubtedly, AI could have many benefits, such as helping to aid the eradication of war, disease and poverty, the scientists wrote. Creating intelligent machines would be one of the biggest achievements in human history, they wrote, but it "might also be [the] last." Considering that the singularity may be the best or worst thing to happen to humanity, not enough research is being devoted to understanding its impacts, they said.

As the scientists wrote, "Whereas the short-term impact of AI depends on who controls it, the long-term impact depends on whether it can be controlled at all."

Follow Tanya Lewis on Twitter. Follow us @livescience, Facebook& Google+. Original article on Live Science.

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Artificial Intelligence: Friendly or Frightening?

Artificial intelligence can predict the behavior of Congress.

schools/iStock Photo

By Matthew HutsonJun. 21, 2017 , 2:30 PM

The health care bill winding its way through the U.S. Senate is just one of thousands of pieces of legislation Congress will consider this year, most doomed to failure. Indeed, only about 4% of these bills become law. So which ones are worth paying attention to? A new artificial intelligence (AI) algorithm could help. Using just the text of a bill plus about a dozen other variables, it can determine the chance that a bill will become law with great precision.

Other algorithms have predicted whether a bill will survive a congressional committee, or whether the Senate or House of Representatives will vote to approve itall with varying degrees of success. But John Nay, a computer scientist and co-founder of Skopos Labs, a Nashville-based AI company focused on studying policymaking, wanted to take things one step further. He wanted to predict whether an introduced bill would make it all the way through both chambersand precisely what its chances were.

Nay started with data on the 103rd Congress (19931995) through the 113th Congress (20132015), downloaded from a legislation-tracking website call GovTrack. This included the full text of the bills, plus a set of variables, including the number of co-sponsors, the month the bill was introduced, and whether the sponsor was in the majority party of their chamber. Using data on Congresses 103 through 106, he trained machine-learning algorithmsprograms that find patterns on their ownto associate bills text and contextual variables with their outcomes. He then predicted how each bill would do in the 107th Congress. Then, he trained his algorithms on Congresses 103 through 107 to predict the 108th Congress, and so on.

Nays most complex machine-learning algorithm combined several parts. The first part analyzed the language in the bill. It interpreted the meaning of words by how they were embedded in surrounding words. For example, it might see the phrase obtain a loan for education and assume loan has something to do with obtain and education. A words meaning was then represented as a string of numbers describing its relation to other words. The algorithm combined these numbers to assign each sentence a meaning. Then, it found links between the meanings of sentences and the success of bills that contained them. Three other algorithms found connections between contextual data and bill success. Finally, an umbrella algorithm used the results from those four algorithms to predict what would happen.

Because bills fail 96% of the time, a simple always fail strategy would almost always be right. But rather than simply predict whether each bill would or would not pass, Nay wanted to assign each a specific probability. If a bill is worth $100 billionor could take months or years to pull togetheryou dont want to ignore its possibility of enactment just because its odds are below 50%. So he scored his method according to the percentages it assigned rather than the number of bills it predicted would succeed. By that measure, his program scored about 65% better than simply guessing that a bill wouldnt pass, Nay reported last month in PLOS ONE.

Nay also looked at which factors were most important in predicting a bills success. Sponsors in the majority and sponsors who served many terms were at an advantage (though each boosted the odds by 1% or less). In terms of language, words like impact and effects increased the chances for climate-related bills in the House, whereas global or warming spelled trouble. In bills related to health care, Medicaid and reinsurance reduced the likelihood of success in both chambers. In bills related to patents, software lowered the odds for bills introduced in the House, and computation had the same effect for Senate bills.

Nay says he is surprised that a bills text alone has predictive power. At first I viewed the process as just very partisan and not as connected to the underlying policy thats contained within the legislation, he says.

Nays use of language analysis is innovative and promising, says John Wilkerson, a political scientist at the University of Washington in Seattle. But he adds that without prior predictions relating certain words to successthe word impact, for examplethe project doesnt do much to illuminate how the minds of Congress members work. We dont really learn anything about process, or strategy, or politics.

But it still seems to be the best method out there. Nays way of looking at bill text is new, says Joshua Tauberer, a software developer at GovTrack with a background in linguistics who is based in Washington, D.C., and who had been using his own machine-learning algorithm to predict bill enactment since 2012. Last year, Nay learned of Tauberers predictions, and the two compared notes. Nays method made better predictions, and Tauberer ditched his own version for Nays.

So how did the new algorithm rank the many (failed) bills to repeal the Affordable Care Act? A simple, base-rate prediction would have put their chances at 4%. But for nearly all of them, Nays program put the odds even lower.

Read the rest here:

Artificial intelligence can predict which congressional bills will pass - Science Magazine

Artificial intelligence could set off a third world war, but humans will win the battle, according to Alibaba founder Jack Ma.

"The first technology revolution caused World War I," Ma told CNBC in an interview that aired on Tuesday. "The second technology revolution caused World War II. This is the third technology revolution."

Workers and employers are increasingly defined by data unless governments show more willingness to make "hard choices."

Ma said humans will ultimately win the battle against an artificial intelligence takeover, however, as machines will never have the wisdom and experience that comes with being human.

"Wisdom is from the heart," Ma said. "The machine intelligence is by the brain [...] You can always make a machine to learn the knowledge. But it is difficult for machines to have a human heart."

The goal of artificial intelligence should be making machines that do things humans cannot do, rather than making them like humans, Ma said. While "we know the machine is powerful and stronger than us," humans will rise above the impending wave of data and artificial intelligence.

"Humans will win," Ma said. "In 30 years ... we'll see us surviving. "

CNBC's Anita Balakrishnan contributed to this report.

Read more here:

Jack Ma: Artificial intelligence could set off WWIII, but 'humans will win' - CNBC