If all the information available on Google fit on a USB flash-drive, what would happen if we could plug it directly into the brain? What if we could translate our brainwaves into complete texts, or could connect ourselves to a machine to multiply our mental capacities? Although these examples are still science fiction, the technology that tries to make them possible is not. The connection will be achieved through the so-called brain-to-computer interface.Credit: Natural Science Foundation Javier Mínguez and Luis Montesano, researchers from BitBrain, a company specializing in applied neuro-technologies, explain to OpenMind that "it is not that we are close to connecting our brains to technologies to interact with the exterior. It is already a reality."

From analyzing millions of satellite images to finding healthy strains of plant microbiome, these startups have raised over $500M to bring AI and robotics to agriculture. Agricultural tech startups have raised over $800M in the last 5 years. Deals to startups using robotics and machine learning to solve problems in agriculture started gaining momentum in 2014, in line with the rising interest in artificial intelligence across multiple industries like healthcare, finance, and commerce. Smart money VCs like Bessemer Venture Partners, Accel Partners, Khosla Ventures, Lux Capital, and Data Collective have invested in general-purpose drone and computer vision companies with a focus on agricultural applications, like DJI and Orbital Insight, as well as ag tech startups like Blue River Technology. Big corporations like Monsanto and Syngenta, which are active ag tech investors, have also backed companies like Resson and previously mentioned Blue River Technology.

MIT has created an artificial intelligence algorithm which can accurately tell you the recipe behind a dish after being shown no more than a picture. With the emergence of social media, it is not only the spread of information which has grown but also the popularity of image sharing. Everything from cat pictures to cupcakes bombards the internet every day, but there may now be a use for the latest delicious meal your friend has shared on their social network accounts -- as you may be able to cook it yourself just by having access to the picture. On Thursday, MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) said that a new artificial intelligence-based algorithm has been developed which can analyze still images of food in order to detect the likely ingredients and suggest a recipe to create the dish. The average recipe has nine ingredients and the most common ingredients found in today's dishes are salt, butter, sugar, olive oil, water, eggs, garlic cloves, milk, flour, and onion.

Okay, I will admit that I didn't know much about AI or machine learning before Spring 2017 SourceCon: We Control The Robots. Fortunate for me, I had a chance to interview Summer Husband and do a write up on her presentation. She is the director of data science for Randstad. As she explained to me the science behind machine learning, I became convinced that this is the future and that we as sourcers need to add machine learning to our sourcing toolbelts. Let me tell you why you should.

If Beijing has its way, the future of artificial intelligence will be made in China. The country laid out a development plan on Thursday to become the world leader in A.I. by 2030, aiming to surpass its rivals technologically and build a domestic industry worth almost $150 billion. Released by the State Council, the policy is a statement of intent from the top rungs of China's government: The world's second-largest economy will be investing heavily to ensure its companies, government and military leap to the front of the pack in a technology many think will one day form the basis of computing. The plan comes with China preparing a multibillion-dollar national investment initiative to support "moonshot" projects, start-ups and academic research in A.I., according to two professors who consulted with the government about the effort. The United States, meanwhile, has cut back on science funding.

Computer superintelligence is a long, long way from the stuff of sci-fi movies, but several high-profile leaders and thinkers have been worrying quite publicly about what they see as the risks to come. Our economics correspondent, Paul Solman, explores that. ACTOR: I want to talk to you about the greatest scientific event in the history of man. ACTOR: Are you building an A.I.? ACTRESS: Do you think I might be switched off? ACTRESS: Why is it up to anyone?

Resolving abstract anaphora is an important, but difficult task for text understanding. Yet, with recent advances in representation learning this task becomes a more tangible aim. A central property of abstract anaphora is that it establishes a relation between the anaphor embedded in the anaphoric sentence and its (typically non-nominal) antecedent. We propose a mention-ranking model that learns how abstract anaphors relate to their antecedents with an LSTM-Siamese Net. We overcome the lack of training data by generating artificial anaphoric sentence--antecedent pairs. Our model outperforms state-of-the-art results on shell noun resolution. We also report first benchmark results on an abstract anaphora subset of the ARRAU corpus. This corpus presents a greater challenge due to a mixture of nominal and pronominal anaphors and a greater range of confounders. We found model variants that outperform the baselines for nominal anaphors, without training on individual anaphor data, but still lag behind for pronominal anaphors. Our model selects syntactically plausible candidates and -- if disregarding syntax -- discriminates candidates using deeper features.

Marketers are the store managers of the online retail world: it's up to them to ensure that visitors are having a good time and finding what they need. The challenge is insight: online store managers find it much harder to see what's really going on in the shop, compared to their real world counterparts. It's not immediately obvious why a particular product isn't selling online, or how customers are getting lost or sidetracked on their way to the checkout. That's why, over the past few years, companies in a variety of sectors have focused their attention on enhancing their user experience (UX). Often seen as a qualitative measure of how users'feel' while on websites, UX has historically been difficult to measure.

Retro consoles are a popular option for video game companies right now, especially after Nintendo's recent NES Classic and Super NES Classic reboots. But could Nintendo be dipping back into the well for a third time for a Nintendo 64 Classic? In Europe's Intellectual Property Office, Nintendo recently filed for four trademark applications on controller design icons. Three of the icons are for the Nintendo Switch, original Nintendo and Super Nintendo controllers, but as NeoGAF pointed out, a fourth application is for the Nintendo 64 controller. While the icons themselves might not seen notable, analysts are interested because of where they're often applied. More importantly, these icons have previously been used as branding on recent Nintendo console packing.

A few years ago, I wrote about a fascinating Italian project to use mobile phone data to predict the onset of bipolar disorder. The notion was that the sensors built into the average smartphone are ideal for picking up on the mood changes users undergo as they occur. For instance, the manic stage of the condition is typified by hyperactivity, which can manifest itself in rapid speech, high levels of movement, and excessive phone usage. People in depressive states, by contrast, tend to show similar behaviors, albeit at the other end of the spectrum. It isn't the only work utilizing AI to help those with bipolar disorder, as a recent paper from the University of Cincinnati outlined an approach to accurately predict treatment outcomes by using AI.