Biological and Hybrid Robots: The Video

The combination of biological systems with machines presents numerous issues around both possibilities and ethics. Many have described animals as biological machines. We as humans have continuously attempted to mimic the behavior of creatures, ranging from modeling of early flight on the behavior of birds, down to mimicry of insect behavior in robotics. The next step is to use our growing understanding of biology and robotics together.

It is inevitable that we will achieve some integration between biological and machine components. There have already been many experiments with inserting chips beneath the skin, as well as with creating prosthetic limbs driven by electric signals from the brain. But one area that could be highly significant is use of biological neurons to create artificial intelligence guidance for robotic machines.

While this area is fraught with problems of development and understanding, the capacity to culture large numbers of biological neurons presents possibilities for increasing our understanding of complex thought as well as providing a possible engine for robots of the future. Such a biological “brain” would likely be easier to integrate into human consciousness than purely mechanical neurons. A great deal of further experimentation and understanding are required.

At present biological artificial intelligence is at a very low-level, as illustrated in these videos. They point to limitations in our current understandings of how neurons interact in creating thought. These concepts are gradually drawing the curtains open on a more complex vision of biological AI. But it will likely take a many years until these systems are sufficiently capable and well enough understood to serve a useful function.

Here we have provided three videos related to biological neurons and biological muscle fiber guiding mechanical systems.

Robot with a biological brain (MagicBulletTV)

Heart Cells Are Bringing Robots To Life (DNews)

Robot with a rat brain (New Scientist)

Qualcomm Buys NXP for IoT and Cars

In one of the biggest technology deals in recent years, semiconductor giant Qualcomm has signed an agreement to buy NXP Semiconductors N.V. for about $47 billion. Netherlands-based NXP is most noted for its automobile silicon products. NXP is the fifth-largest non-memory semiconductor supplier, and a leading supplier for secure identification, automotive and digital networking products. It was the co-founder (with Sony) of Near-Field-Communications (NFC), and inventor of the I²C interface used for attaching peripherals to microcontrollers.

These capabilities put NXP in the center of IoT and smart automobile technology. Combining with Qualcomm, whose ARM processor, wireless networking technology, and overlaps in NFC and automotive technologies seems a good fit, this move will expand Qualcomm capabilities as well as opening new markets for embedded silicon solutions.

The NXP release details what the company brings to the table:

Mobile: A leader in mobile SoCs, 3G/4G modems and security.

Automotive: A leader in global automotive semiconductors, including ADAS, infotainment, safety systems, body and networking, powertrain and chassis, secure access, telematics and connectivity.

IoT and Security: A leader in broad-based microcontrollers, secure identification, mobile transactions, payment cards and transit; strength in application processors and connectivity systems.

Networking: A leader in network processors for wired and wireless communications and RF sub-segments, Wave-2 11ac/11ad, RF power and BTS systems.

Qualcomm and Intel have both been moving rapidly into the IoT building blocks territory in recent years, and the developing automobile silicon market is a critical testbed for evolving concepts in this area. To paraphrase Willie Sutton’s famous quote, “that’s where the money is.” Autonomous and electric car frenzies are pushing a variety of technologies to create complete digital environments on fully-managed wheels.

As digitization is proving in so many areas, developments shaping the IoT easily move between categories with the speed of invention. Ultimately, neuromorphic and other specialized cognitive chip designs such as Qualcomm’s Zeroth will line up with these device interfaces to create a new generation of smart things.

But that’s another story.

Guest Blog: Machines Are Taking All The Jobs? What Decision-Makers Say And Do

Original blog link:

Machines Are Taking All The Jobs? What Decision-Makers Say And Do

by Gil Press

A new PwC survey provides fresh and illuminating data on the burning questions of the day: Are machines going to take over our jobs? And how much do we rely (or over-rely) today on machines, automation, and algorithms?

Experts are confident that machines are going to replace many workers. A much-quoted report from Oxford University has estimated that “about 47% of total US employment is at risk” for being fully automated. The machine threat to employment is even greater in developing economies—a recent report from Oxford estimates that 77% of jobs in China and 69% of jobs in India are “at high risk of automation.”

But maybe estimating the type of jobs that the machines are going to replace is the wrong approach. Tom Davenport, who just published a book on strategies for coping with automation, Only Humans Need Apply: Winners and Losers in the Age of Smart Machines (co-authored with Julia Kirby), told the Wall Street Journal recently: “Computers don’t tend to replace whole jobs; they replace specific tasks.”

The McKinsey Global Institute (MGI) agrees: “…a focus on occupations is misleading. Very few occupations will be automated in their entirety in the near or medium term. Rather, certain activities are more likely to be automated, requiring entire business processes to be transformed, and jobs performed by people to be redefined.”

MGI estimates that 45% of work-related tasks can be automated. This finding does not bode well for knowledge workers who were sure their cognitive skills could not be automated and that they will always outrun the machines. Even CEOs, according to MGI, spend over 20% of their time on activities that can be automated with current technology.

What has been missing in this discussion is data on how much we rely (or don’t) on machines today, rather than estimates based on experts’ assessments of how automation-prone are various occupations and activities. Specifically, has the era of big data and increasingly sophisticated algorithms changed the nature of business decision-making? What is the extent by which business executives rely on machines today when they make strategic decisions?

A new PwC survey of more than 2,100 business decision-makers across more than 10 countries and 15 industries sheds new light on these questions. It frames the discussion as follows: “Executives who once relied firmly on their intuition and experience are now face-to-face with machines that can learn from massive amounts of data and inform decisions like never before.”

59% of the decision-makers PwC surveyed say that the analysis they require relies primarily on human judgment rather than machine algorithms. That means that 41% already tend to rely more on algorithms than their own experience, judgment, and intuition. “We are not talking about pricing a seat on an airline,” says (via email) Dan DiFilippo, Global & US Data and Analytics Leader at PwC. “We are talking about big, strategic decisions that almost certainly involve some combination of human and machine, but clearly we see a significant involvement of the machine.”

The most interesting findings are about the type of decisions that tend to be assisted by machine algorithms and the ones that rely more on human judgement. In the chart above, “respondents who answered closest to zero are nearest to the survey’s overall average reliance on analysis from machine algorithms and human judgment. The farther away from the center point, the greater reliance on either mind or machine,” says PwC.

“Shrinking existing business” was deemed by survey respondents as the type of decision that relies most on human judgement and “Investment in IT” as the one relying most on algorithms. “Investment in IT,” says DiFilippo, “can cover many areas including shop floor automation, CRM systems, HR systems, risk management systems, etc., all of which have varying degrees of machine algorithms and can be assessed by machine algorithms.”

The breakdown of results by country offers a striking juxtaposition of China and Japan with the former as the country/region relying more than others on machine algorithms and the latter as the country/region second only to Central and Eastern Europe in its reliance on human judgement. One would think that China and Japan will have similar attitudes toward and use of algorithms in decision-making but this is apparently not the case. It’s possible, however, that the results are due to different interpretations of the survey questions. Says DiFlippo: “We don’t have a precise answer or explanation for this—we are still working to gather more on this front.”

Finally, the breakdown of results by industry shows that different economic sectors differ in the degree by which decision makers rely on their own judgement vs. relying on machine algorithms. Conclude DiFilippo: “Involving the machine can help reduce/eliminate bias (at the individual, department or organization level), add more accuracy and/or more computing power to crank through a high volume of scenarios that human can’t do (or can’t do in a timely manner), and importantly—and the data supports this—there is a sense that the machine can help de-risk the strategic decision… we see that those who had a high degree of machine algorithms felt a high degree of managed and known risks.”

So should we search for the right mix of minds and machines in the context of a specific decision or should we succumb to a universal McAfee’s Law and agree that “as the amount of data goes up, the importance of human judgment should go down”? What’s your experience with trusting machine algorithms rather than your own judgment?

Gil Press

Gil Press is Managing Partner at gPress, a marketing, publishing, research and education consultancy. Previously, he held senior marketing and research management positions at NORC, DEC and EMC. Hewas Senior Director, Thought Leadership Marketing at EMC, where he launched the Big Data conversation with the “How Much Information?” study (2000 with UC Berkeley) and the Digital Universe study (2007 with IDC). He blogs at and Twitter: @GilPress

Invisible Chinese Robot Show: The Video

The 2016 World Robot Conference is now drawing to a close at Beijing International Convention Center, and the most prominent media coverage has been of a few soccer robots falling over in the included RoboCup Challenge between University of Texas and the University of New South Wales.

Actually, according to the IEEE Release, the Conference was about the technological development and industrial application of intelligent robots. With the objective “To develop high-level academic exchanges and to demonstrate the latest achievements with regard to world robot study, its applications in key areas, as well as the innovative development of smart society, ” according to the release.

The Conference was expected to draw 100,000 visitors, with 2000 research institutions represented from Germany, the USA, Korea, Japan, Canada, France, and Israel. Contests included a Self-driving Car Challenge Contest, World Unmanned Aerial Vehicle Challenge, International Water Robot Contest, Robot Star Challenge and the RoboCup Challenge.

Here are the highlights of the RoboCup challenge, which represents an ongoing interest across the globe in using robots of a variety of types (in this case, humanoid) to autonomously play soccer. This involves huge challenges of stability, coordination, goal setting and action.

Australian and U S robots clash at Beijing RoboCup Challenge soccer finals (NewsToday NT)

The RoboCup was only a small part of the show, however, which demonstrated growing Chinese interest in this area, as well as highlighting a wide variety of robotic initiatives.

World Robot Conference 2016: Hello to the future (CCTV News)

Various smart robots make debut at 2016 World Robot Conference (CCTV English)

Intersection of 3D Printing and AI: The Video

Both 3D printing and AI will have important impacts in the continuing advance toward “Industry 4.0”. In combination, however, the results could be both spectacular and unpredictable. Both of these technologies focus upon adaptability and rapid response. They also bring whole new possibilities for mechanisms into play. Designing new devices that can be created, replicated, and even evolve rather quickly presents formidable challenges. Additionally, the way in which these devices interact with their surroundings could create whole new possibilities for changing our environment.

This time, the videos are somewhat mixed, focusing upon flexibility and design issues that will demand advanced analytics. They represent a somewhat distant edge of current development, but are notable in reminding us that AI is likely to become a ubiquitous part of our environment, and 3D printing, with its great and growing flexibility, will be a part of that environment, too.

Videos are from the web, selected from those with the best video presentation of the technology, as well providing insight into current developments. The first video shows an advanced robot design created with AI, and capable of great flexibility immediately out of the printer. The second demonstrates a concept of Building Information Modeling that mixes AI, virtual reality, and 3D printing to create a planning, testing and observation lab for building design. The third video shows a multi-material 3D printing technique based upon mealtime analytics, and the fourth shows a method of applying a kind of flexibility and rudimentary “intelligence” in the printed object itself–with all the advanced analysis and design that will entail.

First-ever 3-D printed robots made of both Solids and Liquids (MIT CSAIL)

Artificial intelligence meets construction through Building Information Modeling (Arirang News)

MultiFab: Vision-Assisted Multi-Material 3D Printing (MIT CSAIL)

4D Printing: Shapeshifting Architecture (Wyss Institute at Harvard University)

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.

Machine Learning: The Video

Machine Intelligence is an important part of the disciplines that we associate with artificial intelligence. This field has evolved significantly since the first neural networks were created in 1943. Advances have created sophisticated pattern recognition based on multi-layered “deep learning” approaches that are powering today’s cognitive systems. Combined with the growing processing power of computers and GPUs, this area is expanding quickly to support powerful new algorithms and new silicon, such as neuromorphic chips.

As with many concepts in the AI and robotics area, video presentations provide some of the best ways to approach the topic. Machine learning does require more explanation, however, so this video selection provides two illustrative approaches and two discussions.

As usual, we have selected material that is watchable and uses video to enhance explanation. We do not own these videos; they are freely available on the web. Neither do we endorse any particular point of view or product line. We like the videos; they do a good job in covering the subject, and we hope you will like them, too.

Feel free to comment in our Comments section, and feel free to suggest other material that you feel enhances the understanding of analytics concepts.

What is Machine Learning (Guru99)

MarI/O – Machine Learning for Video Games (Seth Bling)

Deep Learning: Intelligence from Big Data (Stanford/VLAB ) Panel

Hello World – Machine Learning Recipes #1 (Google Developers) Lecture

Envestnet Buys Wheelhouse Analytics: Harbinger of What’s to Come?

Financial technology firm Envestnet has just acquired Wheelhouse Analytics after purchasing another data firm called Yodlee. While ordinarily, purchase of a niche market analytics firm is not greatly of note, particularly when it is in a relatively small pool of services for investment companies, there are elements of this purchase that provide some food for thought.

Envestnet’s business is to provide wealth management technology and services for investment advisers. It is concerned with a wide range of issues such as portfolio management and wealth optimization options for large investors. It’s clients are investment managers who then use these services to help their clients to achieve better financial outcomes. Wheelhouse Analytics has already been associated with Envestnet for several years, providing contributory services. Wheelhouse is about fee and performance benchmarking, supporting fee rationalization and other services that enable investment managers to use sophisticated analytics to mold their product offerings to the best fit for investment clients.

According to Frank Coates, CEO of Wheelhouse Analytics:

“Combining Wheelhouse capabilities with Envestnet’s Vantage enterprise data management solution, we can deliver the industry’s most comprehensive oversight and surveillance tool to support compliance with the DOL Fiduciary Rule. We also look forward to continuing to invest in enhancing and expanding our current set of solutions.”

One of the critical features is to provide investment managers with services that they could not easily perform for themselves or could not handle cost-effectively. Within this market, a key issue is the growing oversight and regulation from the Department of Labor (DOL). The DOL’s Fiduciary Rule requires financial operators such as brokers and investment salespeople to be able to show that they are suggesting accounts and options that are actually good for clients rather than simply of benefit to the seller. This makes analysis of different investment outcomes extremely valuable. It is another example of how regulation, in general, is likely to impact the advance toward big data analytics.

Regulations can have complex impacts across the firm, some of which cannot easily be extrapolated by simple methods. Increasingly, analytics makes it possible to determine the best means of compliance across a range of possible options.

The other point of interest is increasing provision of smart services for professionals, and its impact upon the services industries. In this case, an analytics solution within the investment community makes it increasingly easy for individual players and freelancers to take on established large corporate competition. Investment salesmen who have relied upon the deal making strategies of their corporate parent could now become independent and rely upon the intelligent assistance of external service providers. Such professional services support will make it increasingly attractive to cut ties with larger firms supported by a new technologies with little of the overhead that prevails in slower moving professional corporate organizations.

While the Wheelhouse acquisition is a small thing in itself, it is reflective of greater changes that may have a significant impact in the workplace and in M&A deals in the years to come.