The AI talent grab is real. This year alone, Pinterest CTO Vanja Josifovski jumped ship to Airbnb, while Pinterest hired Walmart CTO Jeremy King to head up its engineering team. Moreover, all the big tech companies, including Google and Apple, have for some time been vacuuming up AI talent through acquisitions -- a recent CB Insights report noted 635 AI acquisitions since 2010, topped by Apple with 20 acquisitions. Elsewhere, Microsoft turned to online education platforms to help train a new generation of AI students. But while the AI talent pool may be growing, a significant shortage remains.

This Udacity Nanodegree Program that will help you gain the must-have skills for all aspiring data analysts and data scientists. Explore the end to end process of investigating data through a machine learning lens. Learn to extract and identify useful features that can be used to represent your data in the best form. In addition to this, you will also go over some of the most important ML algorithms and evaluate their performance.

Many real-world control problems involve both discrete decision variables - such as the choice of control modes, gear switching or digital outputs - as well as continuous decision variables - such as velocity setpoints, control gains or analogue outputs. However, when defining the corresponding optimal control or reinforcement learning problem, it is commonly approximated with fully continuous or fully discrete action spaces. These simplifications aim at tailoring the problem to a particular algorithm or solver which may only support one type of action space. Alternatively, expert heuristics are used to remove discrete actions from an otherwise continuous space. In contrast, we propose to treat hybrid problems in their 'native' form by solving them with hybrid reinforcement learning, which optimizes for discrete and continuous actions simultaneously. In our experiments, we first demonstrate that the proposed approach efficiently solves such natively hybrid reinforcement learning problems. We then show, both in simulation and on robotic hardware, the benefits of removing possibly imperfect expert-designed heuristics. Lastly, hybrid reinforcement learning encourages us to rethink problem definitions. We propose reformulating control problems, e.g. by adding meta actions, to improve exploration or reduce mechanical wear and tear.

Causal machine-learning is about predicting the net-effect (true-lift) of treatments. Given the data of a treatment group and a control group, it is similar to a standard supervised-learning problem. Unfortunately, there is no similarly well-defined loss function due to the lack of point-wise true values in the data. Many advances in modern machine-learning are not directly applicable due to the absence of such loss function. We propose a novel method to define a loss function in this context, which is equal to mean-square-error (MSE) in a standard regression problem. Our loss function is universally applicable, thus providing a general standard to evaluate the quality of any model/strategy that predicts the true-lift. We demonstrate that despite its novel definition, one can still perform gradient descent directly on this loss function to find the best fit. This leads to a new way to train any parameter-based model, such as deep neural networks, to solve causal machine-learning problems without going through the meta-learner strategy.

Reinforcement learning (RL) has achieved tremendous success as a general framework for learning how to make decisions. However, this success relies on the interactive hand-tuning of a reward function by RL experts. On the other hand, inverse reinforcement learning (IRL) seeks to learn a reward function from readily-obtained human demonstrations. Yet, IRL suffers from two major limitations: 1) reward ambiguity - there are an infinite number of possible reward functions that could explain an expert's demonstration and 2) heterogeneity - human experts adopt varying strategies and preferences, which makes learning from multiple demonstrators difficult due to the common assumption that demonstrators seeks to maximize the same reward. In this work, we propose a method to jointly infer a task goal and humans' strategic preferences via network distillation. This approach enables us to distill a robust task reward (addressing reward ambiguity) and to model each strategy's objective (handling heterogeneity). We demonstrate our algorithm can better recover task reward and strategy rewards and imitate the strategies in two simulated tasks and a real-world table tennis task.

Abstract: Adversarial examples are a pervasive phenomenon of machine learning models where seemingly imperceptible perturbations to the input lead to misclassifications for otherwise statistically accurate models. In this paper we study how the choice of optimization algorithm influences the robustness of the resulting classifier to adversarial examples. Specifically we show an example of a learning problem for which the solution found by adaptive optimization algorithms exhibits qualitatively worse robustness properties against both $L_{2}$- and $L_{\infty}$-adversaries than the solution found by non-adaptive algorithms. Then we fully characterize the geometry of the loss landscape of $L_{2}$-adversarial training in least- squares linear regression. The geometry of the loss landscape is subtle and has important consequences for optimization algorithms.

It's safe to say that technology leaders in higher education won't be bored in 2020. Many of the most pressing trends in IT are playing out in the university space. Colleges are leveraging artificial intelligence to drive student outcomes and adopting smart technologies to enhance campus efficiencies. On the defensive side, IT is battling a rising tide of phishing and ransomware. Here are five trends to watch in higher education IT in the coming year.

At work, AI tells sales reps which accounts they should be pursuing and helps lawyers instantly analyze piles of contracts. Is it any wonder that we're starting to think it might be OK if the machines take over? A recent global survey found that 64 percent of more than 8,000 respondents said they didn't just embrace AI -- they would actually trust it more than their manager. Tony Deigh, chief technology officer at the Cambridge machine-learning-based employment platform Jobcase, understands this impulse. As AI gets better at recognizing complicated patterns from huge troves of data, it could conceivably be applied to many roles, like being a boss. "Would I take career advice from a machine?

Many universities and technical colleges have done a ton of research, development and exploration into Artificial Intelligence, knowledge systems and machine learning. Establishing good relations with academic institutions can be helpful. In addition to the desirability of supporting students and professors, organizations may derive great benefit from access to current research at these institutions. Academic institutions can also be a valuable source of information and an eventual source of skilled personnel. There is, however, a considerable difference in results that may be obtained from assigning major system design and development responsibilities to academic institutions as opposed to consultants. In spite of these potential difficulties, there have been successful collaborations between academic institutions and industrial organizations in the development and application of Al technology.

A great technological shift is on the verge of occurring very soon. Disruptive Artificial Intelligence technologies are going to change the world and human labour will be replaced by robot workers and the shift has in-fact started. This mind-blowing course introduces you to the concept of Artificial Intelligence usage in the workplace along with providing you practical examples of the different platforms that deploy the same for automation. You will learn about the numerous Human Resources tools and usage of these in Artificial Intelligence, along with sales-based AI tools that can help you close the deal. You will be also introduced to Virtual chatbots that look like human and do all the automation and support work for you in any industry you are in. We will also look at a particular case study of a company leveraging human robots as receptionists to free up tasks for the real employees.