DISCOVERING and harnessing fire unlocked more nutrition from food, feeding the bigger brains and bodies that are the hallmarks of modern humans. Google's chief executive, Sundar Pichai, thinks his company's development of artificial intelligence trumps that. "AI is one of the most important things that humanity is working on," he told an event in California earlier this year. "It's more profound than, I don't know, electricity or fire." Hyperbolic analogies aside, Google's AI techniques are becoming more powerful and more important to its business.

Today, it is no longer a question of adopting AI or not. Instead, ask yourself if you and your sales team are ready for the inevitable. Artificial intelligence for business is a reality. If your goal is to forge ahead and lead in your field, then you need to adapt to a workplace where AI plays a crucial role. As J.J. Kardwell, founder and CEO of predictive marketing software company EverString, puts it: "Growth-focused sales organizations of every size and stage cannot afford to ignore the benefits of AI-assisted sales."

Reinforcement Learning (RL) refers to both the learning problem and the sub-field of machine learning which has lately been in the news for great reasons. RL based systems have now beaten world champions of Go, helped operate datacenters better and mastered a wide variety of Atari games. The research community is seeing many more promising results. With enough motivation, let us now take a look at the Reinforcement Learning problem. Reinforcement Learning is the most general description of the learning problem where the aim is to maximize a long-term objective.

Jarosław Gowin, who is a deputy prime minister as well as minister of science and higher education, said that Polish companies dealing with artificial intelligence had been on the market for 25 years. He added that Poland had talented young mathematicians and IT specialists. Gowin was speaking during the Impact'18 conference in the southern Polish city of Kraków earlier this week. An encounter with humanoid robot Sophia, developed by Hong Kong-based Hanson Robotics and appearing in Poland for the first time, was one of the highlights of the two-day gathering. Thousands of people attended the conference to debate innovation and digital trends.

Nov. 7, 2013: Pakistani Taliban leader Mullah Fazlullah is seen on television at a coffee shop in Islamabad. The Pakistani Taliban leader known for beheading police officers and even ordering the assassination of Nobel Peace Prize winner Malala Yousafzai has been killed by a U.S. drone strike, Afghanistan's Defense Ministry says. Mohammad Radmanish told the Associated Press on Friday that Mullah Fazlullah, the ruthless insurgent leader, died along with two other terrorists a day earlier in the Marawara district along the Afghanistan-Pakistan border. A statement attributed to U.S. Forces-Afghanistan spokesman Lt. Col Martin O'Donnell said an American "counterterrorism strike" was carried out in the region targeting "a senior leader of a designated terrorist organization," but did not say whether it had killed anyone. Fazlullah previously ordered the bombing and beheadings of dozens of opponents when his band of insurgents controlled Pakistan's picturesque Swat Valley from 2007 until a massive military operation routed them in 2009.

What are some open datasets for machine learning? We at Gengo decided to create the ultimate cheat sheet for high quality datasets. First, a couple of pointers to keep in mind when searching for datasets. Kaggle: A data science site that contains a variety of externally-contributed interesting datasets. You can find all kinds of niche datasets in its master list, from ramen ratings to basketball data to and even seattle pet licenses.

MIT doctoral student Maria Bauza is exploring providing tactile feedback to robots. With the push of a button, months of hard work were about to be put to the test. Sixteen teams of engineers convened in a cavernous exhibit hall in Nagoya, Japan, for the 2017 Amazon Robotics Challenge. The robotic systems they built were tasked with removing items from bins and placing them into boxes. For MIT graduate student Maria Bauza, who served as task-planning lead for the MIT-Princeton Team, the moment was particularly nerve-wracking.

Much of the literature on fair classifiers considers the case of a single classifier used once, in isolation. We initiate the study of composition of fair classifiers. In particular, we address the pitfalls of na{\i}ve composition and give general constructions for fair composition. Focusing on the individual fairness setting proposed in [Dwork, Hardt, Pitassi, Reingold, Zemel, 2011], we also extend our results to a large class of group fairness definitions popular in the recent literature. We exhibit several cases in which group fairness definitions give misleading signals under composition and conclude that additional context is needed to evaluate both group and individual fairness under composition.

We consider the problem of learning a low-rank matrix, constrained to lie in a linear subspace, and introduce a novel factorization for modeling such matrices. A salient feature of the proposed factorization scheme is it decouples the low-rank and the structural constraints onto separate factors. We formulate the optimization problem on the Riemannian spectrahedron manifold, where the Riemannian framework allows to develop computationally efficient conjugate gradient and trust-region algorithms. Experiments on problems such as standard/robust/nonnegative matrix completion, Hankel matrix learning and multi-task learning demonstrate the efficacy of our approach. A shorter version of this work has been published in ICML'18 (Jawanpuria and Mishra, 2018).

Sequential learning studies the problem of learning tasks in a sequence with restricted access to only the data of the current task. In the setting with a fixed model capacity, the learning process should not be selfish and account for later tasks to be added and therefore aim at utilizing a minimum number of neurons, leaving enough capacity for future needs. We explore different regularization strategies and activation functions that could lead to less interference between the different tasks. We show that learning a sparse representation is more beneficial for sequential learning than encouraging parameter sparsity regardless of their corresponding neurons. We particularly propose a novel regularizer that encourages representation sparsity by means of neural inhibition. It results in few active neurons which in turn leaves more free neurons to be utilized by upcoming tasks. We combine our regularizer with state-of-the-art lifelong learning methods that penalize changes on important previously learned parts of the network. We show that increased sparsity translates in a performance improvement on the different tasks that are learned in a sequence.