Zachary C. Lipton and Jacob Steinhardt have written an outstanding paper, entitled Troubling Trends in Machine Learning Scholarship, that focuses on patterns that are a trend in the scientific literature from the machine learning community: the failure to distinguish between explanation and speculation, the failure to identify the sources of empirical gains, the mathiness (the use of mathematics that obfuscates or impresses rather than clarifies), and the misuse of language. Those are the same issues that we find, PERMANENTLY!, in the failed efforts to define what (machine) intelligence is and is not, since the very origins of AI. Lipton and Steinhardt elaborate on the following possible causal factors: a complacency in the face of progress, the rapid expansion of the community, the consequent thinness of the reviewer pool, and misaligned incentives of scholarship vs. short-term measures of success. They don't stop there, they even provide suggestions for authors, publishers, and reviewers, and conclude that It is tremendously important that each and every AI researcher, practitioner, author, reviewer, publisher, investor, journalist, user, leader, student, educator, and enthusiast is aware of these implications. We will never advance AI in the right direction by carrying flawed scholarship all the way with us.

Brandman University is taking a new approach to adult education, focusing on student competencies and work experience rather than transcripts when deciding which students to admit and when they graduate. Brandman already is working with companies, including Walmart and Discover, to offer employee-education programs. The Irvine, California-based nonprofit university accepts subject matter expertise and experience as course credit, making it easier for working adults to earn college degrees and advance their careers. At most conventional colleges, students must fulfill prerequisite courses to earn admission and a set of required courses to earn a degree. Under the Brandman approach, if an applicant has, say, a 20-year career in finance but no formal coursework in finance, "she can now test out of many course requirements, simply by proving her mastery through standard assessments, writing samples, even work projects," says the university's chief financial officer.

Artificial intelligence is already reshaping society as we know it in both business and consumer realms. Early use cases with Alexa, autonomous vehicles and AI-driven supply chains provide just a glimpse of the disruption that AI is poised to deliver in the near future and for years to come. Yet despite all the AI hype and initial successes, it remains in its infancy. That makes now the ideal time for young people to build the knowledge, skill sets and connections they need to capitalize on the fast-growing market for AI jobs and build a strong AI career. One reason is simply practical. Gartner predicts that AI may eliminate 1.8 million jobs by 2020, yet is on track to create 2.3 million new positions.

How do you teach computers to understand language -- not just transcribe human speech, but actually comprehend what someone is saying? It's one of the grand challenges of AI, and we still don't really know the best way to tackle the problem. Facebook's AI research lab, FAIR, has one idea: teach AIs to understand language by getting them to guide virtual tourists around New York City. FAIR is releasing what it calls Talk the Walk, a dataset designed to be used by other researchers. It's comprised of three elements: small maps of New York City neighborhoods (each a couple of blocks wide), 360-degree photos of the same locations, and sample dialogues of humans guiding one another around these neighborhoods. Basically, it's everything you might need to teach an AI to tackle this task itself.

Games generalize the optimization paradigm by introducing different objective functions for different optimizing agents, known as players. Generative Adversarial Networks (GANs) are arguably the most popular game formulation in recent machine learning literature. GANs achieve great results on generating realistic natural images, however they are known for being difficult to train. Training them involves finding a Nash equilibrium, typically performed using gradient descent on the two players' objectives. Game dynamics can induce rotations that slow down convergence to a Nash equilibrium, or prevent it altogether. We provide a theoretical analysis of the game dynamics. Our analysis, supported by experiments, shows that gradient descent with a negative momentum term can improve the convergence properties of some GANs.

We study a general online linear optimization problem(OLO). At each round, a subset of objects from a fixed universe of $n$ objects is chosen, and a linear cost associated with the chosen subset is incurred. We use \textit{regret} as a measure of performance of our algorithms. Regret is the difference between the total cost incurred over all iterations and the cost of the best fixed subset in hindsight. We consider \textit{Full Information}, \textit{Semi-Bandit} and \textit{Bandit} feedback for this problem. Using characteristic vectors of the subsets, this problem reduces to OLO on the $\{0,1\}^n$ hypercube. The Exp2 algorithm and its bandit variants are commonly used strategies for this problem. It was previously unknown if it is possible to run Exp2 on the hypercube in polynomial time. In this paper, we present a polynomial time algorithm called \textit{PolyExp} for OLO on the hypercube. We show that our algorithm is equivalent to both Exp2 on $\{0,1\}^n$ as well as Online Mirror Descent(OMD) with Entropic regularization on $[0,1]^n$ and Bernoulli Sampling. Under $L_\infty$ adversarial losses, in the Full Information case and Semi-Bandit case, analyzing Exp2 directly, gives an expected regret bound of $O(n^{3/2}\sqrt{T})$, whereas PolyExp yields a regret of $O(n\sqrt{T})$. In the Bandit case, analyzing Exp2 directly, gives an expected regret bound of $O(n^{2}\sqrt{T})$, whereas PolyExp yields a regret of $O(n^{3/2}\sqrt{T})$. This implies an improvement on Exp2's regret bound for these settings because of the equivalence. Moreover, PolyExp is minimax optimal in all the three settings as its regret bounds match the $L_\infty$ lowerbounds in \cite{audibert2011minimax}. Finally, we show how to use PolyExp on the $\{-1,+1\}^n$ hypercube, solving an open problem in \cite{bubeck2012towards}.

Artificial intelligence technology in education is no more the future, it is the present. We are already witnessing the impact of technology in the education sector. Increased uses of digital devices, adaptive learning platforms, and engaging lessons have transformed the learning as well as teaching processes. With the help of artificial intelligence technology, the educators today are working on creating activities that can not only ignite the curiosities among the students, but can make their learning experience a memorable one. Although AI can never replace the human teachers, it has successfully given a whole new meaning to the roles and responsibilities of a teacher.

There are lots of education options available online, provided you're a self-starter with the discipline to do a lot of coursework on your own. For example, Microsoft's AI School offers a variety of lessons for developers in everything from text analytics and object recognition to custom neural-network models. The content is angled toward data scientists and developers, and heavily emphasizes the use of Microsoft products (of course) in addition to "universal" A.I. skills. It's also free, although those who want Verified Certificates will need to pay a fee. Microsoft, of course, is far from your only option when it comes to learning about A.I. online, particularly with regard to beginner-level material.

MOUNTAIN VIEW, CA – Across every industry, whether it is manufacturing, healthcare, financial services, and more, companies and organizations of all sizes are realizing that AI has the ability to generate amazing value, transform processes, and uncover insights for smarter decisionmaking. But behind the algorithms that are empowering the use cases are the data scientists who drive the models and analyze the insights. According to a report from IBM, Burning Glass and Business Higher Education Forum, the number of job openings for data and analytics talent will increase by 364,000 to 2,720,000 in 2020. Millions of staff will be needed to fuel the ever-growing demand for AI and machine learning. As a result, there's a growing gap between the supply and demand of AI talent, a gap which has resulted in increasingly higher salaries for those in the field.

Data science internships are highly sought after, as studying machine learning in a classroom setting cannot be compared to applying it to solve real client problems. Yet, hopefuls for data science internships will need to have more than just technical skills under their belts, said Mr Ng Sing Kwan, vice president, data science at e-commerce company Lazada. "A lot of people think that to be a good data scientist, they just need to have good coding skills and understand machine learning statistical concepts. But what they lack--and what we look out for--are problem-solving skills and business acumen," he said. Mr Ng, who transited from a business background to data science, was speaking at a panel discussion titled'What Does It Take To Land The First Data Science Job/Internship?', organised by volunteer-led data analytics community Analytics.Club Singapore in partnership with SGInnovate.