This is a follow up to an article Per wrote last year, Machine Learning in a Week, on how he kickstarted his way into machine learning (ml) by devoting five days to the subject. Follow him on Medium and check out his archive. My interest in ml stems back to 2014 when I started reading articles about it on Hacker News. I simply found the idea of teaching machines stuff by looking at data appealing. At the time I wasn't even a professional developer, but a hobby coder who'd done a couple of small projects.

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 unstructured 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.

This was the title of a very popular book published in 2012, featuring several job interview questions (brain teasers) asked by Google's hiring managers to candidates. They apparently dropped all these questions, as they found out that they were not good indicators of career success. Do you think you are smart enough to work for Google? I had one phone interview with Google long ago, and was rejected right away. The interviewer was just focused on very technical details, and spent all her time arguing about Lasso regression, and was clearly looking for a specialist, dismissing people with a broad range of skills and non-standard approach to solving tech problems.

In this blog post we'll briefly cover the following topics to give you a very basic introduction to machine learning: Don't worry if you're not an expert -- the only knowledge you need for this blog post is basic high school maths. The goal of machine learning is to come up with algorithms that can learn how to perform a certain task based on example data. Let's say we want to write a program to play the game Go. We could write this program by manually defining rules on how to play the game. We might, program some opening strategies and decision rules -- that it's better to capture a stone than not, for example.

Imagine a surgeon asking a Siri-like digital assistant in the operations theater about the options available in a risky operation, based on the patient's medical history matched with a global database of similar cases. The "assistant" comes up with several options in a split second and, the surgeon and his team, choose one that they think is best and proceed. This could be one of the many possibilities that an Artificial Intelligence or A.I. can provide to the healthcare sector. A.I. is poised to become a game changer for the health care sector, according to Steve Leonard, chief executive of SGInnovate, the government entity that supports entrepreneurs leading Singapore's innovation efforts. But convincing doctors, clinicians, nurses, patients and other stakeholders to place their trust in self-thinking machines could be tricky.

According to a study by Oracle, almost 80 percent of businesses have already implemented or are planning to adopt AI as a customer service solution by 2020, and a recent report by Deloitte and Oxford University suggests telesales could be the next to go. Other experts are ambivalent about whether AI is really advanced enough to take over the role of a talented sales representative. AI is already being used in the sales field to respond to basic email or chat inquiries, organize sales interactions, and follow up with leads. However, recent studies show that even the most advanced uses of AI bots still struggle when faced with complex user queries, and experts argue that if rolled out too early, bots may frustrate users and create more problems than solutions. Here's why it's too early for AI to take over the role of sales representative just yet.

Although artificial intelligence (AI) has experienced a number of "springs" and "winters" in its roughly 60-year history, it is safe to expect the current AI spring to be both lasting and fertile. Applications that seemed like science fiction a decade ago are becoming science fact at a pace that has surprised even many experts. The stage for the current AI revival was set in 2011 with the televised triumph of the IBM Watson computer system over former Jeopardy! This watershed moment has been followed rapid-fire by a sequence of striking breakthroughs, many involving the machine learning technique known as deep learning. Computer algorithms now beat humans at games of skill, master video games with no prior instruction, 3D-print original paintings in the style of Rembrandt, grade student papers, cook meals, vacuum floors, and drive cars.1 All of this has created considerable uncertainty about our future relationship with machines, the prospect of technological unemployment, and even the very fate of humanity. Regarding the latter topic, Elon Musk has described AI "our biggest existential threat." Stephen Hawking warned that "The development of full artificial intelligence could spell the end of the human race." In his widely discussed book Superintelligence, the philosopher Nick Bostrom discusses the possibility of a kind of technological "singularity" at which point the general cognitive abilities of computers exceed those of humans.2 Discussions of these issues are often muddied by the tacit assumption that, because computers outperform humans at various circumscribed tasks, they will soon be able to "outthink" us more generally. Continual rapid growth in computing power and AI breakthroughs notwithstanding, this premise is far from obvious.

WASHINGTON – The Intelligence Community is sponsoring a $500,000 prize competition to explore artificial intelligence approaches that would transform the process by which analysts currently support policymakers and warfighters through the research and generation of written products. The Office of the Director of Science and Technology within the Office of the Director of National Intelligence--in partnership with the Office of the Under Secretary of Defense for Intelligence--is launching its first challenge contest, "Xpress," to explore AI-based opportunities for generating analytic products that surpass those crafted by traditional, highly-trained IC analysts. Leveraging private-sector momentum in this area will help ensure that the IC continues to employ cutting-edge methodologies and tools to quickly warn and inform policymakers in an ever more demanding and complex global environment. "Given the pace and breadth of international activity, the IC's analytic community is increasingly challenged to provide policymakers and warfighters with timely information and analysis on a growing number of targets and issues," said Dr. David Isaacson, DS&T program manager for the challenge. "Xpress serves a critical role in exploring the potential for'machine analytics' to enhance existing IC support to our nation's decision makers, ultimately paving the way for analytic production to occur on a timeline and scale that IC analysts and their customers can scarcely imagine today."

In this work, we present a methodology that enables an agent to make efficient use of its exploratory actions by autonomously identifying possible objectives in its environment and learning them in parallel. The identification of objectives is achieved using an online and unsupervised adaptive clustering algorithm. The identified objectives are learned (at least partially) in parallel using Q-learning. Using a simulated agent and environment, it is shown that the converged or partially converged value function weights resulting from off-policy learning can be used to accumulate knowledge about multiple objectives without any additional exploration. We claim that the proposed approach could be useful in scenarios where the objectives are initially unknown or in real world scenarios where exploration is typically a time and energy intensive process. The implications and possible extensions of this work are also briefly discussed.

This paper investigates the phase retrieval problem, which aims to recover a signal from the magnitudes of its linear measurements. We develop statistically and computationally efficient algorithms for the situation when the measurements are corrupted by sparse outliers that can take arbitrary values. We propose a novel approach to robustify the gradient descent algorithm by using the sample median as a guide for pruning spurious samples in initialization and local search. Adopting the Poisson loss and the reshaped quadratic loss respectively, we obtain two algorithms termed median-TWF and median-RWF, both of which provably recover the signal from a near-optimal number of measurements when the measurement vectors are composed of i.i.d. Gaussian entries, up to a logarithmic factor, even when a constant fraction of the measurements are adversarially corrupted. We further show that both algorithms are stable in the presence of additional dense bounded noise. Our analysis is accomplished by developing non-trivial concentration results of median-related quantities, which may be of independent interest. We provide numerical experiments to demonstrate the effectiveness of our approach.