There are many different types of sites that provide a wealth of free, freemium and paid data that can help audience developers and journalists with their reporting and storytelling efforts, The team at State of Digital Publishing would like to acknowledge these, as derived from manual searches and recognition from our existing audience. Kaggle's a site that allows users to discover machine learning while writing and sharing cloud-based code. Relying primarily on the enthusiasm of its sizable community, the site hosts dataset competitions for cash prizes and as a result it has massive amounts of data compiled into it. Whether you're looking for historical data from the New York Stock Exchange, an overview of candy production trends in the US, or cutting edge code, this site is chockful of information. It's impossible to be on the Internet for long without running into a Wikipedia article.

When we made episodes of #CoffeeWithAGoogler covering Google's Artificial Intelligence Yourself (AIY) project, we wanted to test the product with a group of high school kids. We were blown away by the results -- come check it out for yourself!

"Ultimately lawyers are like programmers, only difference being, they code/write in legal language, which in most cases is English," says Shashank Bijapur, co-founder of AI driven legaltech startup SpotDraft and a former Wall Street Lawyer. It is this belief which led Shashank, a Harvard Law School graduate, from the echelons of Wall Street to finding a startup in the legaltech space which would be capable of using AI to read through contracts, organise, manage and finally, analyse them. But we are jumping the gun here. Roll back a few years to Shashank's Wall Street days where he saw day in and day out what lawyers did. I have seen upfront what lawyers do and how they work. I realised that the process is too time consuming and riddled with inefficiencies.

At #WebSummit 2017, I was part of a panel on what the future will bring in 2030 with John Vickers from Blue Abyss, Jacques Van den Broek from Randstad and Stewart Rogers from Venture Beat. John talked about how technology will allow humans to explore amazing new places. Jacques demonstrated how humans were more complex than our most sophisticated AI and thus would be an integral part of any advances. And I focused on how the current technological changes would look amplified over a 10–12 year period. After all, 2030 isn't that far off, so we have already invented all the tech, but it isn't widespread yet and we're only guessing what changes will come about with the network effects. As William Gibson said, "The future is here, it's just not evenly distributed yet."

NVIDIA (NVDA) is moving fast in its mission to make AI (artificial intelligence) affordable. The company is expanding its efforts to create more AI researchers and developers through its DLI (Deep Learning Institute). Don't miss the next report. You are now receiving e-mail alerts for new research. A temporary password for your new Market Realist account has been sent to your e-mail address.

We develop SHOPPER, a sequential probabilistic model of market baskets. SHOPPER uses interpretable components to model the forces that drive how a customer chooses products; in particular, we designed SHOPPER to capture how items interact with other items. We develop an efficient posterior inference algorithm to estimate these forces from large-scale data, and we analyze a large dataset from a major chain grocery store. We are interested in answering counterfactual queries about changes in prices. We found that SHOPPER provides accurate predictions even under price interventions, and that it helps identify complementary and substitutable pairs of products.

Deep learning is the state-of-the-art in fields such as visual object recognition and speech recognition. This learning uses a large number of layers and a huge number of units and connections. Therefore, overfitting is a serious problem with it, and the dropout which is a kind of regularization tool is used. However, in online learning, the effect of dropout is not well known. This paper presents our investigation on the effect of dropout in online learning. We analyzed the effect of dropout on convergence speed near the singular point. Our results indicated that dropout is effective in online learning. Dropout tends to avoid the singular point for convergence speed near that point.

We are building technology that enables existing robot hardware to handle a much wider range of tasks where existing solutions break down, for example, bin picking of complex shapes, kitting, assembly, depalletizing of irregular stacks, and manipulation of deformable objects such as wires, cables, fabrics, linens, fluid-bags, and food. To equip existing robots with these skills, our software builds on the latest advances in deep reinforcement learning, deep imitation learning, and few-shot learning, to all of which the founding team has made significant contributions. The result isn't just a new set of skills in the robot repertoire, but teachable robots, that can be deployed for new tasks on short turn-around. The background here will be familiar to anyone who has followed Abbeel's research at UC Berkeley's Robot Learning Lab (RLL).

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