Skills for Data Science and Machine Learning: The Video

Here we look at the skills and employment opportunities that are surfacing as we move into an era of advanced analytics and ubiquitous AI.

The videos are a mixture of seminars and discussions avaialable under standard YouTube license, with landing page descriptions added. Minor edits have been made where necessary.

What Kaggle has Learned from Almost a Million Data Scientists (O’Reilly

Published on May 25, 2017

A talk by Anthony Goldbloom from Kaggle.  Kaggle is a community of almost a million data scientists, who have built more than two million machine-learning models while participating in Kaggle competitions. Data scientists come to Kaggle to learn, collaborate, and develop the state of the art in machine learning. Anthony Goldbloom shares lessons learned from top performers in the Kaggle community and explores the types of machine-learning techniques typically used, some of the tricks he’s seen, and pitfalls to avoid. Along the way, Anthony discusses work habits and skills that help data scientists succeed.

Andrew Ng: Artificial Intelligence is the New Electricity (Stanford Graduate School of Business)

Published on Feb 2, 2017

On Wednesday, January 25, 2017, Baidu chief scientist, Coursera co-founder, and Stanford adjunct professor Andrew Ng spoke at the Stanford MSx Future Forum. The Future Forum is a discussion series that explores the trends that are changing the future. During his talk, Professor Ng discussed how artificial intelligence (AI) is transforming industry after industry.

What Are the Top Skills Needed to Be a Data Scientist? (SAS Software)

Published on Oct 26, 2015

Dr. Goutam Chakraborty of Oklahoma State University outlines the top skills you need to be successful as a data scientist. From SAS Academy for Data Science

Top Data Scientist D J Patil’s Tips to Build a Career in Data (FactorDaily)

Published on Jul 20, 2017

D J Patil, who along with top data scientist Jeff Hammerbacher coined the term “data scientist” tells you what it takes to be a data scientist During the Obama administration, Patil became the first ever chief data scientist to be appointed by the US government.




Recruiters: Mind Your T’s and Q’s

Finding the right person for the growing range of new technologies can be difficult. There are skills shortages in a number of areas, and there is a rush to fill them with obvious talent. In this rush, however,  it is important to remember that the specific skills are of only transitory value, and requirements will shift as the next new thing comes along.

Digitization is rapidly converging traditionally disparate processes and technologies, creating a need for a new kind of worker. In focusing constantly on specific technical skills, we may be weakening the ability to understand the broader context that must fuel innovation. There needs to be input from the Arts, from global experience, and from the imagination. This demands a different type of learning.

In the late nineties, “T-shaped skills” were introduced, with the vertical bar representing dept of skills and the horizontal bar, the ability to work across disciplines. This was useful in a structured and deterministic world. But we are now in a time of vast changes, shifting skills requirements, and new pressures from robotics and AI. It’s time for a new, and complementary, concept.

Some years ago, I drew a cartoon about recruitment fads ( in reaction to “T-shaped” assumptions. In it, I introduced “Q-shaped” skills as “roundness of knowledge with a squiggly bit underneath.”

Although this was partly in jest, it does raise a significant point. In a converged world, “T-shaped” is no longer enough. Just as Steve Jobs drew from calligraphy for PC invention, exposure to a much wider range of knowledge is increasingly essential for innovation. Imagination and ingenuity are also at a premium.

Certainly, “T-shaped” skills will always continue to be important. But handling the growing possibilities of digital convergence creates a need for the nuanced “Q-shaped” skills that focus upon the big picture and its imaginative possibilities.

We have already seen how over-emphasis upon rote learning and tests can increase “T” and diminish “Q” skills. Companies lacking in the former will have trouble meeting the needs of the moment; companies lacking in the latter will fail to envision  the opportunities of the future.

We need to increase our “Q” skills to create the Total Quality workforce of tomorrow.


Baidu Adds xPerception to its AI/VR Stockpile

Leading Chinese Internet search provider Baidu is acquiring US startup xPerception, an AI-based visual perception software and hardware provider for robotics and virtual reality (VR). This provides important talent for the company’s moves into AI, with xPerception co-founders, Dr. Bao Yingze and Dr. Chen Mingyu, who were both key engineers at AR startup Magic Leap. The xPerception team will move to the US and Beijing offices of Baidu Research and continue developing xPerception’s Simultaneous Localization and Mapping (SLAM) technology.

SLAM is critical to visual perception used in a variety of  AI and VR roles, including 3D vision, robotics, drones and autonomous driving. The base of xPerception technology is a 3D visual inertial camera on mobile platforms, with a sophisticated SDK that enables pose tracking, low-latency sensor fusion, and object recognition. This permits self-localization, 3D structure reconstruction, and path planning in new environments. These technologies linking AI and VR are opening new opportunities, as discussed in the recent blog On the Intersection of AI and Augmented Reality.

In addition to integration with Baidu AI and autonomous driving programs, the xPerception acquisition provides high-demand skills and helps to defray concerns over US regulations and immigration policies. There has recently been potential regulatory blockage of Chinese acquisitions, most notably claims that Alibaba Group’s $1.2 billion bid for U.S. firm MoneyGram International poses national security risks. U.S. lawmakers are requesting  review by the Committee on Foreign Investment. Meanwhile, Chinese internet firm LeEco has terminated of a $2 billion bid for U.S. electronics firm Vizio due to regulatory issues. Baidu’s splitting of its AI research between Beijing and California provides assurance that changing US immigration policies will not overly affect research agendas, while providing access to US-based talent and skills networks.

Baidu has been active in AI for some years now, but finding talent in this area is difficult. High-profile chief scientist, Andrew Ng, left the company  in March, making addition of experienced AI/VR staff a priority. Its commitment to this area is further demonstrated by the Chinese government’s  February selection of Baidu to set up a national AI lab. Baidu Research currently maintains four analytics and AI labs: a Silicon Valley AI Lab, the Institute of Deep Learning, the Big Data Lab and the Augmented Reality Lab. According to Ng, Baidu’s AI group now includes about 1,300 people.

The search for AI talent is global, fueled by visions of integrating this rapidly developing technology with an increasing range of business and technology processes. Autonomous vehicles continue to be a driving force. Meanwhile, globalization issues may drive companies to hedge their bets, particularly in China and India. The last large acquisition, Intel’s purchase of Mobileye, split research between Silicon Valley and Israel. (Car Wars: Intel Bags Mobileye).


Car Wars: Intel Bags Mobileye

Intel is acquiring autonomous driving company Mobileye in a deal valued at $15.3 billion, expected to close toward the end of this year. Acquisition of the Israeli firm, whose technology is used by  27 companies in the auto industry, represents a number of interesting issues in the self-driving vehicle technology race.

Intel has been pursuing autonomous vehicle technology, but this initiative–one of the 10 largest acquisitions in the tech industry–brings it front and center. The key to Mobileye’s autonomous solution lies in its silicon. Mobileye has developed its EyeQ® family of system-on-chip (SoC) devices, which support complex and computationally intense vision processing while still maintaining low power consumption. Mobileye is currently developing its fifth generation chip, the EyeQ 5, to act as the visual central computer for fully autonomous self-driving vehicles expected to appear in 2020. The EyeQ chips employ proprietary computing cores optimized for computer vision, signal processing, and machine learning tasks, including deep neural networks. These cores are designed specifically to address the needs of Advanced Driver Assistance Systems (ADAS).

As a chip developer focusing upon providing the building blocks for technology, its traditional role, Intel is moving forcefully in this direction, partly as a result of growing competition in embedded machine learning from the likes of Nvidia and Qualcomm, both of which are also moving into the autonomous vehicle area. Self-driving cars are the nexus of development in machine learning due to the huge profit expectations of the automobile, transportation, and logistics industries. Evolution of autonomous vehicles, particularly with deep learning capabilities in silicon, will also create additional pressure on skills for artificial intelligence across all industry sectors, as well as creating an explosion in innovation and speeding development of these systems.

Intel intends to form an autonomous driving unit combining its current Automated Driving Group (ADG) and Mobileye. The group will be headquartered in Israel and led by Mobileye’s co-founder, Amnon Shashua, currently Mobileye’s Chairman and CTO; and a professor at Hebrew University.

According to the combined press release:

This acquisition will combine the best-in-class technologies from both companies, spanning connectivity, computer vision, data center, sensor fusion, high-performance computing, localization and mapping, machine learning and artificial intelligence. Together with partners and customers, Intel and Mobileye expect to deliver driving solutions that will transform the automotive industry.

The new organization will support both companies’ existing production programs and build upon the large number of relationships that Mobileye maintains with OEMs,  automobile industry tier 1 suppliers, and semiconductor partners.

Intel’s interests in this deal are likely to be diverse. Among the potential benefits are:

  • Potentially four terabytes of data per hour of data to be processed, creating large-scale opportunities for Intel’s high-end Xeon processors and mobilize latest generation of SOC’s.
  • Moving to Israel, where Intel already has a significant presence, potentially isolates its research and testing from the competitive hotbed of Silicon Valley, shielding employees from poaching. It also avoids current immigration issues.
  • Additional competitive advantages within AI and embedded deep learning, which are the next generation targets of Intel’s silicon competition.

It is worth noting that this is a general boost to autonomous vehicles that will inevitably lead to greater concentration of resources in this field.  Although relying upon a common supplier of autonomous systems makes sense economically, it also reduces competitive advantages.

The number of companies involved in this sector continues to grow as the implications stretch out through the entire transportation-related sector.  We have covered a number of these systems in recent blogs here (Car Wars: DiDi Chuxing Roars into the Valley with 400 Million Users, Car Wars: Ford Adds Billion Dollar Investment Acquisition to its AI, All Things Need Autonomy: TomTom Grabs Autonomos, Uber Doubles Down on AI with Geometric Acquisition, Qualcomm Buys NXP for IoT and Cars ). The net result will to be to create a huge rush for talent in the machine learning space, as well as all of the areas related to integration with automobile systems. This will increase the speed of evolution for embedded AI, which will filter rapidly into other areas of business and process enablement though impeded by the availability of talent.

Microsoft Acquires Maluuba for Deep Learning and Talent

Microsoft has announced the acquisition of deep learning company Maluuba, a Canadian startup that focuses upon a comprehensive view of artificial intelligence, and particularly, the understanding of language. Maluuba’s role in Microsoft is likely to involve fortification of the Cortana digital assistant, as well as other voice-understanding initiatives.

In its blog on the subject Microsoft focused on Maluuba’s integration with Microsoft’s overall AI research efforts. The blog by Harry Shuman, Executive Vice President of Microsoft’s Artificial Intelligence and Research Group Microsoft says:

“Maluuba’s impressive team is addressing some of the fundamental problems in language understanding by modeling some of the innate capabilities of the human brain, from memory and common sense reasoning to curiosity and decision making.”

In addition to Maluuba, the acquisition brings a major deep learning player along from Montréal. Yoshua Bengio is a top expert in deep learning and head of the Montréal Institute of Learning Algorithms. He is an adviser to Maluuba and will continue in an advisory capacity, though his position with Microsoft remains unknown. Notably, Bengio has played a central role in making Montréal a center of deep learning.

Maluuba was founded in 2011 by classmates Sam Pasupalak and Kaheer Suleman from the University of Waterloo, in Canada. The name Maluuba came from Waterloo professor Peter A. Buhr. It was a made up name that he used for fictitious programming languages.

The Acquisition

For Maluuba, the acquisition will make it possible to improve AI capabilities generally, and incorporate a range of related technologies. According the co-founders’ announcement:

“So far, our team has focused on the areas of machine reading comprehension, dialogue understanding, and general (human) intelligence capabilities such as memory, common-sense reasoning, and information seeking behavior. Our early research achievements in these domains accelerated our need to scale our team rapidly; it was apparent that we needed to bolster our work with significant resources to advance towards solving artificial general intelligence.”

Maluuba also has a revealing insight on its intended directions on its website:

“We finally saw a great opportunity to apply Deep Learning and Reinforcement Learning techniques to solve fundamental problems in language understanding, with the vision of creating a truly literate machine – one that could actually read, comprehend, synthesize, infer and make logical decisions like humans. This meant we had to heavily invest in research, therefore we started our Research lab in Montréal in late 2015 (in addition to our awesome engineering team in Waterloo). Our research lab, located at the epicentre of Deep Learning, is focused on advancing the state-of-the-art in deep learning for human language understanding.”

How this Helps Microsoft

The acquisition of Maluuba continues Microsoft’s activity and interest in the AI sphere since it created the Artificial Intelligence and Research Group last fall.

The acquisition of Maluuba demonstrates the growing AI focus upon understanding and acting upon commands; a capacity demonstrated by IBM’s Watson, and in a simplified but ubiquitous form, by Amazon’s Alexa (we discussed Alexa here). Language is a key to the current development of AI. Language contains not only the basics of communication which might be understood by Natural Language Processing (NLP), but it also contains all of the nuances of human thought. Watson’s victory in the TV Jeopardy competition some years ago increased awareness of this. It is one thing to understand basic language; a secondary problem is to understand well enough to respond; and the third issue is to understand the question and then to be able to formulate a response which meets human criteria of a sufficient answer.

The practical impact of Microsoft’s acquisition is likely to be fairly minimal at present. This is part of Microsoft’s experimental program and will be incorporated in its research toward developing AI capabilities for the future.

Parsing the Pieces

As the AI revolution continues, we are seeing deep learning become ever important in conjunction with other analytics tools to create a more comprehensive AI solution. What we mean by “artificial intelligence” continues to expand as we develop further understanding of the inner workings of both human and machine thought. Some of this is informed by technology, but it also encompasses and interacts with research initiatives in the biological sciences, as well as with the philosophy of intelligence from the past.

It is critical for major software companies to compete in this area. Research is often acquired through mergers with small startup companies such as Maluuba. We can expect further mergers and acquisitions in this area as the technology expands. AI will gradually assert itself in corporate processes; initially through the user interface, and capability to respond to input in natural language. It will move on toward greater comprehension of audio, graphic, and video formats; and then on to the next step of achieving a greater understanding and increasing ability to provide autonomous response.

AI in Education: The Video

Education and training are absolutely critical to the new economy. Yet we are having increasing difficulties in satisfying these needs. As huge populations demand higher education, institutions are stretched. While the continued movement of education online with MOOCs has created new possibilities, these training programs have suffered from low completion rates and have not proven adequate to meeting the growing crisis. Artificial Intelligence is now opening new avenues for improving the availability and effectiveness of education through a more interactive online experience and more effective processes for meeting educational goals.

The use of AI in education is a natural. Educational institutions have long led research across a variety of topics in technology. In a case of “physician heal thyself,” AI research can be turned to solving the problems of higher education. Jobs are becoming more knowledge- and skills- focused; vast numbers of new workers are joining the workforce globally; barriers are falling constantly as network access makes it possible to perform jobs as easily from Chicago as from Bangalore; and growing demands for success and prosperity, fueled by access to greater information about the rest of the world in every village, are creating a huge and previously untapped market for learning.

The jobs of tomorrow will require new training. Meanwhile, educational institutions are becoming increasingly expensive. Aspirations of the billions of people who need higher learning to achieve success will not be thwarted. As with every other sphere, the needs of the many will drive the market away from the few.

AI offers ways of improving the educational process; of improving interaction with students; of determining appropriate curricula; of tailoring education to personal and institutional needs; and of collaborating with teachers and schools in extending their efforts to a wider community. As we move into a new era, all of these issues need to be explored. AI will not replace teachers or schools; it will simply make it possible for everyone to achieve the learning results that they need to prosper in an increasingly complex, and knowledge-driven world.

Here we will look at a set of videos on this topic, with their published explanations.

Artificial intelligence & the future of education systems (TEDx Talks)

Published on Aug 4, 2016

Dr. Bernhard Schindlholzer is a technology manager working on Machine Learning and E-commerce. In this talk he gave at TEDx FHKufstein, Bernhard Schindlholzer contemplated the implications of ephemeralization – the ability of technological advancement to do “more and more with less and less until eventually you can do everything with nothing” – through artificial intelligence and machine learning. He explores the challenges that this technological approach poses to our economy and, furthermore, how they could be addressed by questioning established norms of our education systems.

Dr. Bernhard Schindlholzer is a technology manager working on Machine Learning and E-commerce.

This talk was given at a TEDx event using the TED conference format but independently organized by a local community.

Artificial Intelligence & Education: Lifelong Learning Dialogue (TEDx Talks)

Published on Jul 7, 2015

In this talk, Prof. Iiyoshi goes head to head with an AI questioning the fate of education and lifelong learning!

Toru Iiyoshi was previously a senior scholar and Director of the Knowledge Media Laboratory at the Carnegie Foundation for the Advancement of Teaching (1999-2008), and Senior Strategist in the Office of Educational Innovation and Technology at Massachusetts Institute of Technology (2009-2011). He is the co-editor of the Carnegie Foundation book, “Opening Up Education: The Collective Advancement of Education through Open Technology, Open Content, and Open Knowledge” (MIT Press, 2008) and co-author of three books including “The Art of Multimedia: Design and Development of The Multimedia Human Body” and numerous academic and commercial articles. He received the Outstanding Practice Award in Instructional Development and the Robert M. Gagne Award for Research in Instructional Design from the Association for Educational Communications and Technology. Currently, he is the director and a professor of the Center for the Promotion of Excellence in Higher Education (CPEHE) at Kyoto University.

This talk was given at a TEDx event using the TED conference format but independently organized by a local community.

Neuroscience, AI and the Future of Education | Scott Bolland (TEDx Talks)

Currently around 63% of students are disengaged at school, meaning that they withdrawal either physically or mentally before they have mastered the skills that are required to flourish in later life. In this talk Scott Bolland explores the science of learning, the mismatch between how we teach and how the brain natural learns, and the important role that artificial intelligence could take in addressing the limitations in our current education system.

Dr Scott Bolland is the founder of New Dawn Technologies, a high-tech software company aiming to revolutionise education through the use of artificial intelligence. He has spent the last 20 years actively researching and teaching in the field of cognitive science – the scientific study of how the mind works – which spans disciplines such as psychology, philosophy, neuroscience, artificial intelligence and computer science. He holds a PhD in this field, as well as a university medal for outstanding academic scholarship.

This talk was given at a TEDx event using the TED conference format but independently organized by a local community.

Dr. James Lester on Artificial Intelligence in Education (Vimeo)

Dr. James Lester is a Distinguished Professor of Computer Science at North Carolina State University, where he is Director of the Center for Educational Informatics.

AI in the Enterprise: New Skills Required

As we move into the post big data era of cognitive computing and artificial intelligence, the shortage of high level analytics skills is likely to become more pronounced. (See The Big Data Skills Gap INFOGRAPHIC.) While some of the shortage issues are now being resolved, the world of artificial intelligence demands new skills and greater availability as AI becomes integrated into the process environment.

In a world in which processes of all types have some level of AI attached, whether as part of the user interface or as part of the operation itself, it becomes necessary to have more employees who understand the underpinnings of this technology. When the autonomous car breaks down, who are you going to call? While big data provides complex analysis of extraordinarily large data sets, the predictive nature and model creation requirements of deep learning and other machine learning techniques will make many operations appear like “black boxes.” Software could make and act on unforeseen decisions that could be detrimental to the business. Understanding the mechanics of artificial intelligence will become as important as understanding the basic operation an automobile engine was in the 20th century.

Unforseen Consequences of AI: A Car Hacking
Unforeseen Consequences of AI: A Car Hacking

AI training and education, must become a priority. The demand for skills is likely to be explosive due to ubiquitous use of these new technologies. Luckily, this is matched by significant changes in corporate training and external education programs. We are moving to a time of continuous learning. New courses related to analytics and advanced artificial intelligence are appearing almost daily. These range from specialty courses provided by vendors of analytics software such as IBM and SAS, to MOOCs and online courses supplementing what is available from conventional universities and academic institutions.

While courses are becoming available, training decisions are not as simple as they might appear. The level of prerequisite knowledge for advancing into machine learning and predictive analytics can present a taller hurdle than entering into conventional big data analytics or expanded use of statistics. Moreover, the prerequisites can easily change and the concentration of classes and courses can alter as the development of these disciplines continues to advance.

Courses are available in programs for certificates and degrees, as well as on a course by course basis. Examples include Deep Learning, from; the Machine Learning Engineer Nanodegree from Udacity and the Stanford Machine Learning course on Coursera, with Andrew Ng. Note that many analytics courses deal with the machine learning side of things as part of a wider discussion of analytics and programming. This is natural due to the close integration between these topics and the need to know and understand the basics of other approaches before being able to grasp the essentials of a cognitive approach. (See Machine Learning: The Video for a few examples.)

For enterprises and HR departments, there will always be the daunting question of whether to hire new workers in these skills with freshly minted degrees and specialty subjects; or to train employees already at work in related disciplines. Hiring provides immediate skills without experience with the enterprise and its systems; training takes an individual off-line for the duration of the training, but can provide a better quality result by adding skills to existing understandings of the workplace in which the new learning is employed.

What is clear is that the upcoming age out of next generation artificial intelligence requires a next generation of artificial intelligence.

Apple Chases Indian AI Talent, Buys Tuplejump

Apple has furthered its interest in Machine Learning with a new acquisition, Indian big data firm Tuplejump. Tuplejump is a company specializing in managing big data, with its most visible project being the open source FiloDB project, which applies machine learning and analytics to large and complex streamed data sets.

This is Apple’s third recent AI acquisition, after Turi Inc. and Emotient. Apple is not commenting, and there’s not a lot of information available. It appears that the initial deal may have been struck as early as June, but it was reported by TechCrunch as recent (Apple acquires another machine learning company: Tuplejump). Apple’s most visible use of AI is in its Siri virtual assistant product line, but the company is investing in a wide range of IoT projects that will use machine learning, including watches and automobiles.

A key issue with small startup purchases in these areas is the “acqui-hire” value, where the real object of acquisition is the personnel. This is particularly critical in technology areas where there is an existing or perceived skills shortage. Most of Tuplejump’s 16 employees were already located on the US West Coast, and the principals had already been working with Apple, so the acquisition points more to how the battle for AI and Analytics talent is being waged than to the specifics of the Tuplejump solution.