Libratus, an AI developed by Carnegie Mellon University, has defeated four of the world's best professional poker players in a marathon 120,000 hands of Heads-up, No-Limit Texas Hold'em poker played over 20 days, CMU announced today (Jan. Libratus led the pros by a collective $1,766,250 in chips.* The tournament was held at the Rivers Casino in Pittsburgh from 11–30 January in a competition called "Brains Vs. The developers of Libratus -- Tuomas Sandholm, professor of computer science, and Noam Brown, a Ph.D. student in computer science -- said the sizable victory is statistically significant and not simply a matter of luck. "The best AI's ability to do strategic reasoning with imperfect information has now surpassed that of the best humans," Sandholm said. "This is the last frontier, at least in the foreseeable horizon, in game-solving in AI." This new AI milestone has implications for any realm in which information is incomplete and opponents sow misinformation, said Frank Pfenning, head of the Computer Science Department in CMU's School of Computer Science. Business negotiation, military strategy, cybersecurity, and medical treatment planning could all benefit from automated decision-making using a Libratus-like AI. "The computer can't win at poker if it can't bluff," Pfenning explained. "Developing an AI that can do that successfully is a tremendous step forward scientifically and has numerous applications.

Although artificial intelligence (AI) has experienced a number of "springs" and "winters" in its roughly 60-year history, it is safe to expect the current AI spring to be both lasting and fertile. Applications that seemed like science fiction a decade ago are becoming science fact at a pace that has surprised even many experts. The stage for the current AI revival was set in 2011 with the televised triumph of the IBM Watson computer system over former Jeopardy! This watershed moment has been followed rapid-fire by a sequence of striking breakthroughs, many involving the machine learning technique known as deep learning. Computer algorithms now beat humans at games of skill, master video games with no prior instruction, 3D-print original paintings in the style of Rembrandt, grade student papers, cook meals, vacuum floors, and drive cars.1 All of this has created considerable uncertainty about our future relationship with machines, the prospect of technological unemployment, and even the very fate of humanity. Regarding the latter topic, Elon Musk has described AI "our biggest existential threat." Stephen Hawking warned that "The development of full artificial intelligence could spell the end of the human race." In his widely discussed book Superintelligence, the philosopher Nick Bostrom discusses the possibility of a kind of technological "singularity" at which point the general cognitive abilities of computers exceed those of humans.2 Discussions of these issues are often muddied by the tacit assumption that, because computers outperform humans at various circumscribed tasks, they will soon be able to "outthink" us more generally. Continual rapid growth in computing power and AI breakthroughs notwithstanding, this premise is far from obvious.

In news more suited to the likes of Amazon, JP Morgan is set to look at customers' spending histories in order to sell them future products or services. Artificial Intelligence in finance is a topic that raises a lot of interest. Although AI is predicted to change whole sectors in finance (from asset management to compliance), I've always thought that the first applications of AI in banks would be much less spectacular – but much more useful – and that would be the tried and tested recommendation engine a la Amazon. CBR and FT reported yesterday that JP Morgan is launching a smart system to assist their sales people and suggest trades to clients – in other words, a recommendation engine for the trading floor. This is a very welcome news – banks are finally using a technology that has been around for more than a decade and brings a real value to their clients.

When it comes to voice recognition software, SoundHound is definitely not as big a name as Google or Amazon -- yet. SoundHound has raised $75 million in support of its efforts to create artificial intelligence systems capable of recognizing complex human voices and commands, Bloomberg reports. Samsung and Nvidia, two companies that have previously partnered with SoundHound, were among the investors. SoundHound already has Houndify, a proprietary AI technology capable of interpreting complex speech and other audio. The latest $75 million investment will go toward expanding this system into new territories and devices.

TechCrunch reports on a new $27 million fund designed to promote research into artificial intelligence in the public interest. The Ethics and Governance of Artificial Intelligence Fund aims to support a "cross-section of AI ethics and governance projects and activities" in the U.S. and around the world. Meanwhile a supplementary paper from the World Economic Forum -- which just released its Global Risks Report 2017 -- raises concerns about weaponized AI, cyberattacks through internet-of-things devices, and the use of biotechnology. Researchers from Oxford University have released a report detailing how the General Data Protection Regulation could heavily impact the rollout of AI and machine learning. Separately, the European Parliament's Legal Affairs Committee has urged the European Commission to create rules around the ethical use and liability of robotics.

The rise of the chatbots continues as Cleo, a virtual money assistant described by its creators as the "Siri of personal finance," has launched on Facebook Messenger for users in the UK, reports Paybefore (Banking Technology's sister publication). The platform connects securely to bank and credit card accounts, enabling users to track spending, set budgets and receive reminders and alerts via text or voice commands. Along with Facebook Messenger, Cleo also is available as a standalone app or through integration with Amazon's Alexa and Google Home. The Facebook Messenger integration is a major step forward for Cleo, which was founded in 2016 and thus far has received $700,000 in funding from angel investors, including Skype founder Niklas Zennström. Currently, Cleo is only available in the UK, but the company is planning expansion into additional markets soon.

The Rivers Casino in Pittsburgh may not seem a likely setting for a major scientific breakthrough. But on Tuesday, it was: Libratus, an AI system developed by Carnegie Mellon University, beat the world's top four human players in a 20-day tournament of Head's-Up No-Limit Texas Hold'em poker. Libratus, developed by Carnegie Mellon's Tuomas Sandholm, a professor of computer science, and Noam Brown, a Ph.D. student in computer science, competed against Dong Kim, Jimmy Chou, Daniel McAulay, and Jason Les in a competition called "Brains Vs. Artificial Intelligence: Upping the Ante"--during which 120,000 hands were played. "This is the last frontier," said Sandholm during a press conference on Tuesday.

It was no surprise that artificial intelligence (AI) and its impact on business was a key talking point at this year's World Economic Forum in Davos, Switzerland (Davos 2017), especially as concern of AI-powered machinery displacing human workers grows. Speaking at an AI panel at Davos 2017, Microsoft CEO Satya Nadella discussed how simple it was to eliminate human input altogether: "its augmentation or replacement – that's a design choice. You can say replacement [of humans] is the goal, or you can say augmentation is the goal." But while Microsoft are developing tech to aid with human interaction, others may be less willing to design AI machinery that interacts with humans, instead opting to replace them altogether. However, the way in which AI is currently being developed is to work alongside individuals, or, as IBM CEO Ginni Rometty at the panel puts it, "in service" of them.

We want to apply new data to our network and classify inputs If we overtrain / overfit our network to our training data then our accuracy will be deceiving. It might work very well for training data, but will not work on test data. In order to prevent overfitting we implement preprocessing techniques and tune our hyper parameters.

The company has developed a kind of "App Store" for medical care complete with 40 predictive and prescriptive apps targeting Physicians, Care Managers, Hospital Ops and Finance teams. Their claim is what used to take months to build and deploy, can now happen in a matter of weeks at a significant cost reduction regarding both deployment and ongoing platform operation. The market opportunity is huge. The United States has the biggest healthcare industry in the world, spending approximately $3 trillion a year. By 2018, healthcare will comprise close to 18% US gross domestic product.