The integration of artificial intelligence and the financial industry has always been a match made in heaven--high volumes, the quantitative aspect of finances, need for expediency and accuracy are ideal for the unique skill-set of AI. But, can it impact the high-risk, high-return world of hedge funds? Several companies think so including Hong Kong-based Aidyia. What is a hedge fund? Today, there are more than 10,000 hedge funds that manage approximately $3 trillion in assets.

The use of fake news stories for political disinformation has become a major concern for governments around the world in the wake of the 2016 U.S. presidential election. The Federal Bureau of Investigation concluded Russia used false news reports, spread through social media, to try to sway voters. Writing these stories still needed someone to sit behind a keyboard. Now OpenAI, a non-profit artificial intelligence research group in San Francisco, has unveiled a machine learning algorithm that can generate coherent text, including fake news articles, after being given just a small sample to build on. The algorithm can be tuned to imitate the writing style of the sample text.

More than 60 years after philosopher Ludwig Wittgenstein's theories on language were published, the artificial intelligence behind Google Translate has provided a practical example of his hypotheses. Patrick Hebron, who works on machine learning in design at Adobe and studied philosophy with Wittgenstein expert Garry Hagberg for his bachelor's degree at Bard College, notes that the networks behind Google Translate are a very literal representation of Wittgenstein's work. Google employees have previously acknowledged that Wittgenstein's theories gave them a breakthrough in making their translation services more effective, but somehow, this key connection between philosophy of language and artificial intelligence has long gone under-celebrated and overlooked. The translation service relies on an algorithm created by Google employees called word2vec, which creates "vector representations" for words, which essentially means that each word is represented numerically. For the translations to work, programmers have to then create a "neural network," a form of machine learning, that's trained to understand how these words relate to each other.

AI research nonprofit OpenAI has created a system that can generate fake text from a single line -- and it's not open-sourcing the code for fear of misuse. OpenAI was cofounded by tech mogul Elon Musk, and its sponsors include Silicon Valley heavy-hitters such as Peter Thiel and Amazon Web Services. Last year it gained the praise of Bill Gates after it built a team of five neural networks capable of beating human players in the computer game "Dota 2." Read more: Bill Gates hails "huge milestone" for AI as bots work in a team to destroy humans at video game "Dota 2" Now the company has created a system, named GPT2, capable of imitating and generating text based on only a sentence. The Guardian's Alex Hern got to play with the system, and tried typing in a single Guardian headline about Brexit. From that headline alone, GPT2 was able to generate quotes from UK Labour leader Jeremy Corbyn as well as a fictional spokesman for Prime Minister Theresa May.

Robotics investments in January 2019 have crossed a minimum of $644 million worldwide, armed with a total of 25 robotics transactions. The $644 million raised in January is lower than the funding into this industry raised in December in tune of $652.7 million. One of the biggest investments in January that is $104 million Series A has been made into the Beijing Auto AI Technology Co. of China. Other notable investments in January 2019 into Robotics include the $100 million JV into Ekso Bionics Holdings Inc. and a $59.61 million Series B funding into China-based NASN Automotive Electronics Co. Here are the Top 10 Investments that ruled the Robotics Technologies space in January 2019.

Facial Recognition is a biometric application which catches a picture and uses it to distinguish people by applying facial analytics and comparing it with the current database. Facial recognition systems are regularly utilized for security purposes, particularly in the surveillance field however as of late the utilization of facial recognition in different applications has advanced. The facial recognition market is driven by expanding criminal exercises, internationally. Aside from it, the expanding safety efforts at ATMs and the growing establishment of facial recognition systems at air terminals, and shopping centers drive the facial acknowledgment market. Silicon Valley's way to deal with facial recognition, utilizing amazing PCs and huge datasets of appearances to prepare profoundly precise programming, is just the start to enter into the security market.

President Donald Trump's new "American A.I. Initiative" is designed to place the United States at the forefront of artificial intelligence research. But the executive order itself is reportedly pretty broad about how the nation can actually achieve global A.I. superiority. According to Axios and other sources, there's no new federal funding allocated for artificial-intelligence and machine learning projects; instead, government agencies are asked to shift existing funding to A.I. research, as well as open up datasets, models, and other resources to researchers and other tech pros--potentially fueling new inventions. The National Institute of Standards and Technology (NIST) is also tasked with creating standards for safe and reliable A.I. systems. Government agencies will introduce fellowships and skills programs that will retrain workers to deal with an A.I.-centric future.

NEW DELHI: Indian companies are shelling out huge premiums for artificial intelligence (AI) talent, as competition intensifies in the job market for a skillset that is hard to find. Everyone from consumer Internet players and technology companies to financial services and automakers is betting big on AI, but the local talent pool for them to tap into is extremely limited. The demand-supply mismatch is driving up salaries. AI professionals are getting 60-80% hikes while switching jobs, compared with an average of 20-30% in other skill areas. Even an entry-level AI role can command a 70%-plus premium over that of a plain vanilla computer science (CS) engineer, say recruitment firms and industry experts.

Anyone who's watched "Bridget Jones's Diary" knows one of her New Year's resolutions is "Not go out every night but stay in and read books and listen to classical music." The reality, however, is substantially different. What people actually do in their leisure time often doesn't match with what they say they'll do. Economists have termed this phenomenon "hyperbolic discounting." In a famous study titled "Paying Not to Go to the Gym," a couple of economists found that, when people were offered the choice between a pay-per-visit contract and a monthly fee, they were more likely to choose the monthly fee and actually ended up paying more per visit.

Natural gas is the most important energy source in Italy: it fuels thermoelectric power plants, industrial facilities and domestic heating. Gas demand forecasting is a critical task for any energy provider as it impacts on pipe reservation and stock planning. In this paper, the one-day-ahead forecasting of Italian daily residential gas demand is studied. Five predictors are developed and compared: Ridge Regression, Gaussian Process, k-Nearest Neighbour, Artificial Neural Network, and Torus Model. Preprocessing and feature selection are also discussed in detail. Concerning the prediction error, a theoretical bound on the best achievable root mean square error is worked out assuming ideal conditions, except for the inaccuracy of meteorological temperature forecasts, whose effects are properly propagated. The best predictors, namely the Artificial Neural Network and the Gaussian Process, achieve an RMSE which is twice the performance limit, suggesting that precise predictions of residential gas demand can be achieved at country level.