This was inspired by a bright high school student that emailed me for advice about his interest in deep learning. I've been trying to find good resources for deep learning, but the field does seem rather cryptic and a bit technically prohibitive for me at this point. If you wouldn't mind, I had a couple of questions I'd love to ask you about learning deep learning: A: Your assessment that most deep learning resources are either too brief or too mathematical is spot-on! My partner Jeremy Howard and I feel the same way, and we are working to create more practical resources. We will soon be producing a MOOC based on the in-person course we taught this autumn in collaboration with the Data Institute at USF.

New York City's entire taxi fleet -- nearly 13,250 vehicles -- could be replaced by just 3,000 ridesharing cars if these services were optimized, according to a new study from the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT). CSAIL's Daniela Rus and her team created an algorithm that crunched data from three million New York City taxi rides, calculating routes and schedules for two-person, four-person, and ten-person vehicles. The results showed that 3,000 four-person cabs could help handle 98 percent of the City's demand (with a waiting time of 2.3 minutes), while 3,000 two-person cabs could handle 94 percent and just 2,000 ten-person vehicles could handle 95 percent. "To our knowledge, this is the first time that scientists have been able to experimentally quantify the trade-off between fleet size, capacity, waiting time, travel delay, and operational costs for a range of vehicles, from taxis to vans and shuttles," Rus said in a press release. "What's more, the system is particularly suited to autonomous cars, since it can continuously reroute vehicles based on real-time requests." Many of today's ridesharing systems, like those used by Uber and Lyft, are relatively inflexible when it comes to planning and assigning routes.

Ditto Labs, Inc., a pioneer in deep learning announced today that Red Fort Capital led a new round of investment in the vision-as-a-service company. Ditto's vision-as-a-service API can find nearly anything appearing in a photo -- a specific product, brand logo, location, demographic profile -- even subjective measures of sentiment -- in near real time. Ditto's service is a high value AI application for thousands of digital business's. "Humans can no longer keep up: ubiquitous cameras are casting off reams of big data. Today, critical business decisions need computers to act on visual data at massive scale and in real-time," said Red Fort Capital CEO Parry Singh.

India's patent laws allow for reverse-engineering of certain technologies. A prime example of this reverse-engineering is in the pharmaceutical space, where Indian pharma companies are allowed to reverse-engineer drugs, especially life-saving ones. These drugs may have been developed by pharma majors in other parts of the world--and then introduced into western markets--after India-based outsourcing firms had helped them out with clinical trials, data gathering and reporting to the US Food and Drug Administration (FDA) or its equivalent to get these drugs passed. Indian courts have continued to allow such reverse-engineering of drugs--famously prompting Bayer AG's then CEO Martin Dekkers to say at a conference a few years ago, "We did not develop this medicine for Indians. We developed it for western patients who can afford it."

In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics. As data science is a broad discipline, I start by describing the different types of data scientists that one may encounter in any business setting: you might even discover that you are a data scientist yourself, without knowing it. As in any scientific discipline, data scientists may borrow techniques from related disciplines, though we have developed our own arsenal, especially techniques and algorithms to handle very large data sets in automated ways, even without human interactions, to perform transactions in real-time or to make predictions. To get started and gain some historical perspective, you can read my article about 9 types of data scientists, published in 2014, or my article where I compare data science with 16 analytic disciplines, also published in 2014. I also wrote about the ABCD's of business processes optimization where D stands for data science, C for computer science, B for business science, and A for analytics science.

The Internet of Things (IoT) is touching every technology sector around the world, and it's having a significant impact on how enterprises and consumers interact with machines and devices. TechRepublic talked to IoT experts in a range of disciplines to find out what they think the biggest trends will be in 2017. Participants were Kevin Curran, IEEE senior member and senior lecturer in computer science at Ulster University; Francesco Cetraro, head of registrations, .cloud; Artificial intelligence, augmented reality, virtual reality, healthcare IoT, industrial IoT, and wearables are some of the topics of conversation about where the Internet of Things is headed in 2017. Diabetics have been waiting for years for better technology to manage their condition. Some got tired of waiting and hacked together an open source hardware and software solution.

If you sell your expertise for a living, you will discover significant benefits in transferring your knowledge to the world of Artificial Intelligence (AI). It is now possible to create an online chatbot with near human characteristics to deliver your intellectual property to the world. From your website your clients will be able to interact with the AI system to receive a personalised experience of the way you deliver your specialist skills. The Chatbot will be able to track the client's progress and adapt the learning experience to suit their individual mood, personality and abilities. Your unique talents will be instantly made available to a global audience 24 / 7. Up until August 2016 the software to build a chatbot has been in prototype with major corporations but now it's going mainstream.

There are additional elements that must be considered for colleges to think about offering full reporting buy-in. First, course offerings are not necessarily sound predictors of courses being taken by students. Second, aggregating job data holistically doesn't offer a view of where the jobs actually are, which plays a major role in what colleges should consider offering and developing. Individual campuses have the capacity to mine employment data from federal and state databases, and to consider how to construct academic offerings around those needs. A national center is a great idea, but could be a bit ahead of its time in execution and utility.

Signed networks allow to model positive and negative relationships. We analyze existing extensions of spectral clustering to signed networks. It turns out that existing approaches do not recover the ground truth clustering in several situations where either the positive or the negative network structures contain no noise. Our analysis shows that these problems arise as existing approaches take some form of arithmetic mean of the Laplacians of the positive and negative part. As a solution we propose to use the geometric mean of the Laplacians of positive and negative part and show that it outperforms the existing approaches. While the geometric mean of matrices is computationally expensive, we show that eigenvectors of the geometric mean can be computed efficiently, leading to a numerical scheme for sparse matrices which is of independent interest.

In this paper, prediction for linear systems with missing information is investigated. New methods are introduced to improve the Mean Squared Error (MSE) on the test set in comparison to state-of-the-art methods, through appropriate tuning of Bias-Variance trade-off. First, the use of proposed Soft Weighted Prediction (SWP) algorithm and its efficacy are depicted and compared to previous works for non-missing scenarios. The algorithm is then modified and optimized for missing scenarios. It is shown that controlled over-fitting by suggested algorithms will improve prediction accuracy in various cases. Simulation results approve our heuristics in enhancing the prediction accuracy.