Quantum AI: The Video

The frequently touted advantages of quantum computing are becoming more possible as early systems begin to appear and researchers begin to explore software. Since this developing technology focuses upon solutions for very large data sets and complex calculations, AI seems a natural application. In reality, the situation is more nuanced. There are, indeed, possibilities—particularly in machine learning—but they may demand a new approach and new types of models.

Quantum AI is now being actively pursued. It could have tremendous implications for solving complex and intractable problems, and could even bring researchers closer to a general AI. At the very least, it will provide competitive advantages to first movers who are able to harness its possibilities. It is still too early to determine how much can be gained over conventional and specialized neuromorphic processors, but recent developments are making it possible to explore this area, and interest is beginning to grow.

In the following videos, we explore quantum AI from a variety of viewpoints, from business to technical detail. The videos are a mixture of presentations and discussions available under standard YouTube license, with landing page descriptions added. Minor edits have been made where necessary.

The Race to Quantum AI (Space And Intelligence)

Published on Mar 11, 2017

The initial enthusing over AI has faded and the sci-fi scenarios are mostly over. Even with the emergence of new machine-learning techniques the ultimate goal of the field—some form of General AI—remains a distant vision. Still, powerful machine learning is spreading into new industries and areas of daily life and will heighten attention on the unintended consequences that may result.

Quantum AI The New Frontier in Artificial Intelligence (Welcome To The Future)

Published on Aug 17, 2017

A talk by Dr. Colin P. Williams, Director of Strategy & Business Development, D-Wave Systems.

Universal Deep Quantum Learning (QuICS)

Published on Oct 6, 2015

Universal Deep Quantum Learning QuICS Workshop on the Frontiers of Quantum Information and Computer Science given by Seth Lloyd (MIT). Quantum systems can generate non-classical correlations that can’t be generated by classical systems. This talk investigates whether quantum devices can recognize patterns that can’t be recognized classically. Deep quantum learning architectures are compared with deep classical learning architectures, and conditions are identified under which universal deep quantum learners can recognize patterns that can’t be recognized by classical learners.

Google’s Quantum AI Lab (The Artificial Intelligence Channel)

Published on Sep 4, 2017

Hartmut Neven talks about possible roles of quantum effects and subjective experience in Artificial Intelligence.





AI in Finance: The Video

Financial Technology (FinTech) is traditionally conservative, but has been using machine learning for some time. The industry is now on the verge of a technology explosion, as AI and blockchain create unique challenges across this sector. AI can provide advantages in risk analysis, fraud detection and in marketing, as well as in predicting market behaviour. Every area of finance has specific interests and risks in moving forward with these projects, from ML-driven hedge funds with limited transparency to credit profiling with potential regulatory issues.

As with many other sectors, marketing and customer relations is the first area in which AI will make its mark. In finance, however, there are numerous areas in which AI will have important repercussions. Following are several videos that look at aspects of FinTech AI.

Finance is one of several vertical industries that we will look at in this series, as we explore the ongoing issues when AI technologies are incorporated into businesses, studies, and lives.

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

Why AI is the Future of FinTech (BootstrapLabs)

Published on Jul 24, 2017

The latest innovation and the positive impact of Artificial Intelligence technologies from the Applied AI Conference, an event for people who are working, researching, building, and investing in Applied Artificial Intelligence technologies and products.

Panel Moderator: Jean-Baptiste Su, Principal Analyst. Atherton Research & FORBES Technology Columnist

Parth Vasa, Head of Data Science, Bloomberg Engineering, Bloomberg LP
Massimo Mascaro, Director, Data Engineering and Data Science, Intuit
Sangeeta Chakraborty, Chief Customer Officer, Ayasdi
Mark Nelsen, Senior Vice President of Risk and Authentication Products, Visa

AI and the Future of Finance (IIF)

Published on Oct 15, 2017

Perspective from  The Institute of International Finance.

IBM Watson on Cognitive Computing & Artificial Intelligence Are Transforming Financial Services (LendIt Conference)

Published on Mar 7, 2017

IBM Watson Group’s Brian Walter shows how ‘Cognitive Computing & Artificial Intelligence Are Transforming Financial Services’ at LendIt USA 2017 in New York City.

LendIt USA is the world’s biggest show in lending and fintech.

The Future of Corporate Finance: Automation powered by SAP Leonardo Machine Learning (SAP)

Published on Jul 20, 2017

Leverage next generation automation technologies to significantly increase the level of automation in your Shared Services Organization and drastically increase the efficiency of your Financial Shared Services staff. Smart automation with machine learning is self-learning and continuously improving, thus eliminating maintenance efforts. Staff can move away from daily routines and focus on strategic tasks such as growth & planning. SAP Leonardo Machine Learning can be a key enabler.








AI Centers Go Global, but SV Still Runs the Show

As artificial intelligence becomes increasingly important for business and AI data scientists become scarcer, there is growing international competition to create AI centers around the world. AI hubs bring together academic research and industry, feed innovation, and attract experts. Silicon Valley in the United States remains the leading contender with its concentration of IT companies and venture capitalists. In the US, the runner up is likely to be considered Boston—home to Harvard, MIT, and Boston Dynamics’ famed robots.

The US is not the only place that is focusing upon AI, however. China has expressed a desire to be number one in AI; a first mover with significant research and innovation. They are investing heavily in this sector, and time will tell whether they are able to achieve this goal—which would be difficult unless US tech industry dynamics change. Other countries have lesser goals, such as specializing in AI within niche markets (automobiles, for example). Some hubs are also centered around particular institutions, government centers, industrial research labs, or specific researchers. It is notable that McKinsey Global Institute (MGI) studies show that in 2016, the US got 66% of external AI investments (40% in Silicon Valley, alone), with China second at 17%, and others more distant.

Following are some of the top and upcoming AI centers around the world.


China intends to become a dominant player in artificial intelligence hoping to create $150 billion industry by 2030. China’s focus is important due to its huge online population — over 750 million people — as well as a tech – savvy population, and a government interested in using AI to build efficiencies in its cities. China is already estimated to have the biggest AI ecosystem after the United States, although still dwarfed in spending and number of companies. With the US cutting back on many areas, China believes that it can surge forth in technology such as AI to gain an edge.

One important center for Chinese AI is the Shanghai’s Xuhui District. Shanghai plans to build a number of AI development centers and has recently hosted the 2017 Global Artificial Intelligence Innovation Summit. The Xuhui AI ecosystem and new innovation center are expected to be completed near 2020. The District currently has more than 120 scientific research facilities, 10 institutions of higher learning and thousands of laboratories and R&D centers. Shanghai will also be creating AI development hubs in its Jingan, Yangpu, Jiading and Baoshan districts. The focus of its AI efforts will include big data, cloud computing, and the Internet of vehicles and robots. Chinese companies pursuing AI ventures are led by the BAT companies of Baidu, Alibaba, and Tencent, plus branches of American companies such as Google, Microsoft, and IBM.


Canada is focusing upon artificial intelligence at the government level. The key city to watch at the moment is Montréal, though Toronto also has AI activity. Montréal has academic resources at the Université de Montréal and at McGill University, plus a growing range of companies locating AI facilities there to take advantage of local talent. Google has an AI lab in Montréal focusing on deep learning related to the Google Brain project. It is also investing more than $3 million USD in the Montreal Institute for Learning Algorithms (MILA), a joint research group created by, McGill  and the Université de Montréal. Microsoft has also invested in Montréal’s burgeoning AI sector in supporting a tech-incubator called Element AI from the Institute for Data Valorization (IVADO). Microsoft is growing research and design in Montréal and investing $7 million in Montréal’s academic community in pursuit of AI. The Université de Montréal has 100 researchers in deep learning and McGill has 50. Montréal has one of the biggest concentrations of university students of all major North American cities and is a Canadian leader in university research.


In Europe, London is the top AI center, supporting a Google presence through the acquisition of British – based Deep Mind in 2014. It was a Deep Mind program that defeated a professional Go player and made headlines a few years ago. London is also home to startups such as Tractable for visual recognition and VocalIQ, a self-learning dialogue API acquired by Apple. London is also home to the Leverhulme Centre for the Future of Intelligence (CFI), an AI research Center open by Stephen Hawking that features a collaboration between Cambridge University, Oxford, Imperial College London, and the University of California at Berkeley.

In the European Union, France is also pursuing AI. The French have more than 100 startups leveraging AI in a variety of applications. France has one of the world’s largest machine learning and AI communities, although it’s best and brightest are often hired by global tech firms. While London is still considered the leading European AI hub, Paris has been making some inroads. Overseas interest includes Facebook’s global AI research center in Paris, and Google’s acquired Moodstocks machine learning startup . French universities and research institutions involved with AI include the French Institute for Research in Computer Science and Automation, and the French National Center for Scientific Research (CNRS). Available talent includes 4000 members of the Paris Machine Learning Group, home of the open source code library for AI, SciKit Learn. Startups include Valeo which has recently launched the first global research center in AI in deep learning dedicated to automotive applications.


In Asia, Singapore is also building and AI hub, which matches its ongoing interest in advanced computing and networking. Singapore is building a smart city architecture based on IBM Watson, and IBM is working with the National University of Singapore (NUS) to offer a Watson-based cognitive computing education program. Singapore’s National Research Foundation (NRF) is investing up to $107 million USD over five years in AI.SG, a national program to promote AI adoption.  Startups include Teqlabs, a new innovation lab focusing on initiatives such as API integration for the financial technology industry.  A new artificial intelligence incubator has been announced by private investment firm Marvelstone Group, which plans to build 100 AI startups per year and attract global AI talent to Singapore.

In the End…

Silicon Valley remains very much in the lead. These are just some of the active global AI centers in a dynamic that includes continuous poaching of AI talent and acquisition of promising startups. It is unlikely that this will change soon, even with current developments in global trade and immigration. But hubs outside of SV will continue to drive AI forward, developing talent and new ideas.


AutoML: The Automation of Automation

Machine to Machine: AI makes AI

The next Big Thing in AI is likely to be use  of machine learning to automate machine learning. The idea that routine tasks involved in developing a machine learning solution could be automated makes perfect sense. Machine learning development is a replicable activity with routine processes. Although total automation is improbable at the moment, even partial automation yields significant benefits. As the ranks of available machine learning experts grow thinner and thinner, ability to automate some of their time-consuming tasks means that they can spend more time on high-value functions and less on the nitty-gritty of model building and reiteration. This will, in theory, release more data scientists to work on the vast number of projects envisioned in an ubiquitous AI environment, as well as making it possible for less proficient practitioners to utilize machine learning routines without the need for extensive training.

Although automated machine learning (AutoML) is appealing, and startups have emerged with various promise, it is clear that this capability is not yet fully developed. There are, however, innumerable systems that are suitable now for use in selected environments, and to solve specific problems. Among these programs are Google AutoML, DataRobot, Auto-WEKA, TPOT, and auto-sklearn. Many are Open Source or freely available. Major analytics firms are also rapidly developing AutoML routines, including Google, Microsoft, Salesforce, and Facebook, and this area is being approached with increasing urgency.

Current AutoML programs mainly take care of the highly repetitive tasks that machine learning requires to create and tune models. The chief current  automation targets are selection of appropriate machine learning algorithms, tuning of hyperparameters, feature extraction and iterative modeling. Hyperparamter tuning is particularly significant because it is critical to deep neural networks. Some AutoML routines have already demonstrated advances on manual human performance insome of these areas. Other processes that could support AutoML, such as data cleaning, are aided by a separate body of machine learning tools that could be added to AutoML In fact, AutoML itself exists in a larger context of applying ML to data science and software development—an effort that shows promise but remains at an early stage.

Even with recent focus on AutoML, the capability of these programs has yet to reach the stage where they could be relied upon to achieve a desired result without human intervention. Data scientists will not lose their jobs in near future; as others have pointed out, humans are still required to set the objectives and verify results for any machine learning operation. Transparency is also of the utmost importance in determining whether the model is accurately selecting useful results or has settled upon an illusion of accuracy.

Currently, many  AutoML programs have operated successfully in test cases, with problems emerging as the size of the data set rises or the required operation becomes more complicated. An AutoML solution must not only embrace a wide range of ML models and techniques; but it must at the same time handle the large amount of data that will be used for testing through innumerable iterations.

AutoML, and, indeed, other forms of auto data science are likely to continue to advance. It makes sense that machines should add a layer of thinking about thinking on top of the specific task layer. A machine driven approach to developing automation of automation makes sense, not only in reducing the human component, but also in ensuring that there is capability to meet the demands of an ever expanding usage of AI in business. Arguabley, development of a more autonomous AutoML would be an important step toward Artificial General Intelligence.

Improvement in this area is likely to be swift, due to the urgency of the data scientist shortage at a time when all companies are advised to invest in AI. There is an ambitious DARPA program, Data-Driven Discovery of Models (D3M), aimed at coming up with techniques that automate machine learning model building from data ingestion to model evaluation. This was begun in June, 2016 and is furthering interest in AutoML approaches. Among AutoML startups, one standout is DataRobot, which has raised $54 million recently, bringing its total funding to $111 million. Meanwhile, there is a growing body of research in Academia, as well as within corporate research teams, focusing upon how to crack a problem that could create something like a user-friendly machine learning platform.