Home » Driverless cars (Page 2)
Category Archives: Driverless cars
A cool new iPad app provides a fascinating tour of robotics. Created by IEEE Spectrum the app lets users explore 126 robots (I’m told it will soon have 158) from 19 countries, with 360-degree views, interactive animations, technical specs, and hundreds of photos, videos, and articles.
Among the robots included in the App are Honda’s Asimo, NASA’s Curiosity Mars rover, and Google’s self-driving car. I really enjoyed watching Nao perform tai chi.
There are also androids, drones, exoskeletons, quadrupeds, and snake robots. The app offers countless hours of exploration and entertainment to anyone interested in learning about robotics. There is also in-depth, technical data about each robot, and some basic information on how robots work and some good advice on how to get started in robotics.
Rodney Brooks, Dean Kamen, and other leading roboticists provide insights about their creations — and even some career advice — in exclusive audio interviews. The app also features a detailed glossary of robotics terms, the app designers version of the timeline of robots and artificial intelligence, and a section where users can choose which robot wins in a “face-off match.”
It’s pretty cool and fun, whilst offering good information on the progressive field of robotics.
Download the Robot App for iPad here…
Google have improved their self-driving cars, the exoskeleton is “the Maserati of the rehab world” and other reads
Google have improved their self-driving cars – “we’ve improved our software so it can detect hundreds of distinct objects simultaneously—pedestrians, buses, a stop sign held up by a crossing guard, or a cyclist making gestures that indicate a possible turn. A self-driving vehicle can pay attention to all of these things in a way that a human physically can’t—and it never gets tired or distracted.”
The Open Robotics Initiative asks: “Will consumers buy the notion of a car that drives itself?” (Camilla Bassani).
What do Robots have in common with the British? It’s more upsetting to lose certain kinds of work to them. (Psychologists at Northwestern University and Harvard Business School). The research paper is here.
The exoskeleton is “the Maserati of the rehab world.” (San Antonio Express-News).
If you have been hearing about Thomas Piketty’s ‘must read’ book, Capital in the Twenty-First Century, you might have heard one of his central tenets is that economic growth is driven by deep structural factors related to demographics and technology rather than policy changes.
It is feasible that between 2017 and 2025 we will see much of this economic growth Piketty has documented brought about by advances in robotics, and yes there will also be inequality that he has referenced, but there are at least 5 areas in robotics that can have a positive impact on society, economically and functionally.
Robotics is at an inflection point — a bend in the curve where many technologies that used to be found only in science fiction are becoming everyday reality.
I have documented 5 areas in robotics that will lead to structural change below. I’m deliberately omitting Industrial Robots from this list, although I do believe that more flexible robots such as those from Universal Robots, Unbounded Robotics and Baxter from Rethink Robotics will have a major impact on the workplace. Estimating the global manufacturing labor costs at $6 trillion annually, McKinsey forecast that advanced robotics could have an economic impact on the manufacturing sector of between $720 billion to $1.45 trillion annually.
Likewise I am omitting ‘service robots for personal and domestic use’ such as robots that will help the elderly or take over household chores. Whilst there are some advances in this area, and many agree it could be a trillion dollar market, the early growth of what is likely to be a multi-billion dollar business will come from automation in our homes with the Internet of Things, Roomba and companion robots for elderly care (More than 1,000 Paro therapeutic robots are being used in Japanese hospitals and nursing homes and the same number is claimed to be used in Denmark). Many trials have shown that robots can have a very positive impact on elderly care and I do believe this segment will grow considerably in the coming decades, despite the misgivings from a recent Pew study which indicated some 65% of American respondents to their survey consider robot caregivers would be ‘worse’ for society.
Technologies like 3D printers will begin to unleash breakthroughs in manufacturing, enabling smaller batches of highly customized products at declining price points. Whilst I believe that 3D printing will be a huge market – it does not fit the mould of pure robotics, although the two can complement each other.
I have also omitted military uses of robotics from this list, a market that is already significant and set to ‘explode economically’ over the coming years.
The 5 areas in Robotics, which are already here, that I believe will have a major economic impact and help to transform society over the next decade or so are:
- Medical Procedures, Operations and Health
- Prosthetics and Exoskeletons
- Artificial Assistants
- Driverless Cars
To quote William Gibson: “the future is already here — it’s just not evenly distributed.”
Unmanned Aircraft Systems (UAS), or drones, are currently principally used by the military, but there is a growing demand for non-military usage in the civil environment for a number of governmental functions, like policing, border control, search and rescue, fire fighting, ground traffic surveillance, and pollution control. There is also a strong recognition that lightweight low altitude drones can be a valuable solution in commercial ventures such as farming, logistics, mapping, real-estate sales and inspection, oil and gas pipeline monitoring cinematic filming and security monitoring.
The list of potential uses is vast. Whilst regulations are being discussed in the US and Europe, drones have already being deployed for prescription drug delivery in Germany, crop spraying and inspection in farming, wildlife protection in Africa, drug monitoring and border control and policing in various States and energy companies use drones to check the undersides of oil platforms for corrosion and repairs.
The issue is not whether these products will be adopted once the airspace is integrated, but at what rate.
Earlier this year Business Insider forecast that 12% of an estimated $98 billion (equivalent to $11.76 billion) in cumulative global spending on aerial drones over the next decade will be for commercial purposes.
In a comprehensive report the Association for Unmanned Vehicle Systems International (AUVSI) predicted that drones could have a cumulative $82 billion economic impact on the US alone between 2015 and 2025.
On average analysts indicate the commercial and civil small and lightweight drone market could deliver some $10 to $15 billion in global sales by 2020.
The US Federal Aviation Authority estimates as many as 7,500 small commercial drones will be in use, in the US alone, within five years once the necessary regulations are in place.
The next 5 years for drones is very promising. Expect to see drones becoming part of society’s information infrastructure as News agencies, TV companies, photographers, real estate agents, moviemakers, industrial giants, pizza deliveries, logistic companies, local governments, agriculture and others embrace drone technology.
- Medical Procedures and Operations
The US Roadmap for Robotics indicates that several major societal drivers for improved healthcare access, affordability, quality, and personalization can be addressed by robotics technology. The Report states: “It is essential to continue to develop and deploy robot systems for improvement in medical procedures and to reduce the overall cost of care.”
I split Medical Robots into three areas: Diagnostic systems, Robot-assisted surgery and therapy and Rehabilitation systems.
Medical robotics is considered one of the success-stories of service robotics and has great potential to revolutionize clinical practice by:
- Facilitating medical processes by precisely guiding instruments, diagnostic equipment and tools for diagnosis and therapy.
- Improving safety and overall quality of the medical surgery
- Enhancing the cost-effectiveness of patient care
- Improving the training and education of medical personnel through the use of simulators
- Promoting the use of information in diagnosis and therapy.
Surgical robots improve the accuracy of procedures and thus reduce the complication rates in surgeries. Apart from being accurate, robotic procedures also offer significant cost savings in terms of pre- and post-operation care costs and length of stay at hospitals. There are large numbers of academic papers attesting to the superior outcomes delivered by medical robotics and much analysis on the cost benefits.
IBM’s Watson may become the best diagnostician in the world and be greatly in demand contributing billions to IBM’s sales whilst potentially saving millions of lives.
The global medical robotic systems market was worth $5.48 billion in 2011 and is expected to reach $13.6 billion in 2018, growing at a compounded annual growth rate of 12.6% from 2012. Surgical robots are expected to enjoy the largest revenue share.
The costs and the benefits of medical robots are significant and I believe this sector will continue to grow enormously.
- Robotic Prosthetics and Exoskeletons
Every day, at least 500 people in the United States undergo an operation to amputate one or more of their limbs. More than 80 percent of those surgeries are vascular-related, caused by conditions such as diabetes or heart disease. According to the Amputee Coalition two million American live with the loss of a limb, the number is expected to double in the coming decades as people live longer.
Prosthetics and exoskeletons offer major improvements in the life of people that may have lost a limb, or have another movement disability. Many modern prosthetics now contain microprocessors, sensors and actuators to improve their functionality.
The field of prosthetics is now evolving into making exoskeletons; these wearable, ‘bionic devices’ enable wheelchair users to walk again. Professor Illah Nourbakhsh says prosthetics does far more than just allow someone to walk. “We are headed to where people will have robotic legs instead of a wheelchair,” he says. “It changes the relationship we have by being able to physically see eye to eye with someone, how a whole conversation goes.
The economic market is currently quite small, somewhere around $100 to $150 million, however with the recent advances of prosthetics and exoskeletons it is expected to grow considerably to over $1.5 billion in the next 3 to 5 years and higher still thereafter.
- Artificial Assistants
This domain has the largest possible early impact on the largest number of people. Artificial Intelligence pioneers such as Google Director of Engineering Ray Kurzweill have indicated anyone with a smartphone or tablet will be using ‘cognitive assistants’ by 2017.
The European Union have provided estimations of the 2013 AI market at €700 million (or $959 million), and expect it to grow exponentially over the coming years, exceeding €27 billion (or $35 billion) by 2015. Much of this will be in cognitive artificial assistants.
Google, Microsoft, Apple, Intel and IBM are spending hundreds of millions of dollars in research and development costs to advance the capabilities of these cognitive assistants and capture market share.
Google CEO Larry Page further acknowledged his company’s efforts to pursue AI for the sake of increased productivity through ‘Google Now’ at the TED 2014 conference. Whilst explaining the rationale for Google’s acquisition of DeepMind he said: “Imagine if this kind of intelligence was thrown at your schedule.” This is also something that has been echoed by Google Executive Chairman Eric Schmidt and many others in the industry.
As I wrote in my Harvard Business Review article: The more I use this technology the more it recognizes how I break down tasks and the times of day I am most productive, ensuring that I am most efficient on high priority tasks. The ability of today’s cognitive assistants is really quite remarkable, but it is just the beginning.
Thanks to continued progress by A.I. researchers, the long-imagined potential of cognitive assistants is finally arriving. As robots become increasingly intelligent, so too will we.
- Driverless Cars
Autonomous vehicles, including the iconic Google self-driving cars, will be on the road commercially before 2018. The long-term impact on society of self-driving cars and other autonomous vehicles will be a radical change in how we commute. There will also likely be a sharp reduction in traffic accidents, the majority of which are caused by human error.
Gary Silburg and Richard Wallace of KPMG have written: Driverless cars “technology could provide solutions to some of our most intractable social problems — the high cost of traffic crashes and transportation infrastructure, the millions of hours wasted in traffic jams, and the wasted urban space given over to parking lots, just to name a few.”
Quantifying the economic impact over the next decade is likely to be in the tens of billions of dollars.
Driverless cars have the potential to fundamentally alter transportation systems by averting deadly crashes, providing critical mobility to the elderly and disabled, increasing road capacity, saving fuel, and lowering emissions. By 2035 to 2050 Morgan Stanley predicts annual $1.3 trillion in savings in the United States (with over $5.6 trillions globally) from driverless cars.
There are still many obstacles before driverless cars are available commercially but advances are being made and they could be with us sooner than we think.
Just like robotics, virtual desktop hosting is a boon. You can access your favorite Windows Applications from anywhere on any device(PC/Mac/Android) from CloudDesktopOnline.com. Add many more cloud services to the same desktop from Apps4Rent.com
It’s a very exciting time in robotics, representing huge opportunities; which will have a very positive affect on society. As these technologies become integral to our daily life we will see the benefits even more.
The Future of Artificial Intelligence – In Conversation with Cognitive Psychologist with Gary Marcus (PBS)
DARPA’s New Biotech Division Wants to Create A Transhuman Future (i09.com)
Will a World of Driverless Cars be Heaven or Hell (Atlantic Cities)
Daniel Dewey of The Future of Humanity Institute Oxford, Thinking Carefully About Artificial Intelligence (Podcast and interesting resources)
Robot exoskeleton lets girl lift her arms, reach for the stars (CNN)
Make that 6 reads…
In November 2013 Morgan Stanley announced their blue paper report: “Autonomous Cars: Self-Driving the New Auto Industry Paradigm.” The authors predicted trillions in savings but the announcement provided little data on where those savings would come from. However, thanks to a research note released yesterday on Tesla Motors, Inc. (TSLA’s New Path of Disruption) Morgan Stanley provided an extract from the initial report which provides an outline of how they arrived at the annual $1.3 trillion in savings in the United States (with over $5.6 trillions globally).
Nearly every major auto manufacturer has initiated research and development of automated vehicle systems (semi-autonomous) and self-driving cars. Perhaps the most notable example, Google engineers have already recorded hundreds of thousands of miles in vehicles modified with advanced automated vehicle technology.
Preparing for driverless cars and cars with advanced connectivity technology makes up a significant portion of the $100 billion the global auto industry spends on research and development.
The research and development spend is a reflection of the auto industry’s inevitable change towards self-driving cars which Morgan Stanley says:
“Are no longer just the realm of science fiction. They are real and will be on roads sooner than you think. Cars with basic autonomous capability are in showrooms today, semi-autonomous cars are coming in 12-18 months, and completely autonomous cars are set to be available before the end of the decade.”
The total savings of over $5.6 trillion annually are not envisioned until a couple of decades as Morgan Stanley see four phases of adoption of self-driving vehicles. Phase 1 is already underway, Phase 2 will be semi-autonomous, Phase 3 will be within 5 to 10 years, by which time we will see fully self-driving vehicles on the roads – but not widespread usage. The authors say Phase 4, which will have the biggest impact, is when 100% of all vehicles on the roads will be fully autonomous, they say this may take a couple of decades.
The authors do add: “However, Phase 4 could come sooner than we think. If the government, the auto industry and other entities choose to accelerate adoption to access the full socioeconomic benefits of autonomous cars.”
Quantifying the Economic Benefits
The societal and economic benefits of autonomous vehicles include decreased crashes, decreased loss of life, increased mobility for the elderly, disabled and blind and decreases in fuel usage. The large potential savings, which they estimate at $1.3 trillion per year should accelerate the adoption of self-driving vehicles.
They outline five key areas where the cost savings will come from: $158 billion in fuel cost savings, $488 billion in annual savings will come through a reduction of accident costs, $507 billion is likely to be gained through increased productivity, reducing congestion will add a further $11 billion in savings, plus an additional $138 billion in productivity savings from less congestion.
The authors indicate the $1.3 trillion is a base case estimate and indicate a bear case scenario of $0.7 trillion savings per annum in the United States and a Bull case scenario of US$ 2.2 trillion per year.
This authors are careful to point out that this is a rough estimate and does not account for the cost of implementing autonomous vehicles (one-time), offsetting losses, and investment implications. It also assumes 100% adoption of self-driving vehicles to achieve the potential savings indicated.
Fuel savings: $158 billion per year
Today’s cars, using cruise control and driving smoothly can deliver fuel economy savings of between 20 to 30 percent. Self-driving cars and autonomous vehicles will be more fuel efficient as they will be on cruise control 100 percent of the time, this factor along with improved aerodynamic styling and lighter weight material and other new technological advances cause the authors to conservatively predict:
An autonomous car can be 30% more efficient than an equivalent non-autonomous car… If we were to reduce the nation’s $535 gasoline bill by 30%, that would save us $158 bn.
Accident savings (including injuries and fatalities) $563 billion per year
The authors refer to various reports, such as the World Health Organization estimated 1.24 million deaths globally due to vehicle accidents.
According to the US Census, there were 10.8 million motor vehicle accidents in the US in 2009 (the last year for which data is available).
According to the US DOT, these accidents resulted in over 2 million injuries and 32,000 deaths. Morgan Stanley indicate that human error has been the main determinant in over 90 percent of these accidents.
There is a total cost of $625 billion per year in the US due to motor vehicle-related accidents. If 90% of accidents are caused by driver error, taking the driver out of the equation could theoretically reduce the cost of accidents by 90%. This could save $563 bn (90% of $625 bn) per year.
Productivity gains: $422 bn per year
This is the area I consider most subjective. The authors claim that people will improve productivity as they will be able to work in their cars en-route to work, meetings, etc. The report does provide some pretty compelling statistics.
Congestion savings: $149 bn per year
Referring to a report by the European Commission that congestion costs 1 percent of GDP, the authors believe there will be less cars on the road, due to traffic pooling and better use of cars which will reduce congestion, freeing us up to be more productive.
Fuel Savings from Vehicle Traffic Congestion Avoidance
In summary — The authors believe that full penetration of autonomous cars could result in social benefits such as saving lives, reducing frustration from traffic jams, and giving people more flexibility with commuting or leisure driving.
“These benefits also have significant potential economic implications. And the implications are truly significant – the $1.3 trillion of value potentially generated by autonomous cars amounts to over 8 percent of the entire US GDP, as well as 152 percent of the US Defense budget and 144 percent of all student loans outstanding.”
There is considerable uncertainty concerning autonomous vehicle benefits, costs and travel impacts, this Morgan Stanley research adds significantly to the debate as we move towards fully automated vehicles.
Just like robotics, virtual desktop hosting is a boon. You can access your favorite Windows Applications from anywhere on any device(PC/Mac/Android) from CloudDesktopOnline.com. Add many more cloud services to the same desktop from Apps4Rent.com
For the Morgan Stanley report refer to the WSJ (research report from Morgan Stanley reference at “uses the word “utopian” 11 times.”)
There is much speculation about Google’s intentions with its acquisition of eight robot companies in the fall of last year. What has been missing in this speculation is just how much it has spent on the eight companies.
The acquisitions of the robot companies appear to have been completed in the last quarter of 2013, they were announced in December.
At the end of the third quarter of 2013 Google stated in their financial reporting that they had completed twenty-one acquisitions with a total value of $1,338 billion. They confirmed that $969 million of this was spent on the purchase of mapping company Waze.
The remaining $369 million was spent on purchasing an additional twenty companies.
None of these were material so the company does not have to account for them individually in its regulatory filings.
Some of these acquisitions can be found online. The largest of them was Channel Intelligence for which Google reportedly paid $125 million. Then there are lots of smaller acquisitions ranging from file sharing app, Bump for $35 million, motion detector device Flutter, which cost $40 million. The acquisition of Wavii for $35 million enhances Google’s natural language technology. They boosted their cloud computing capabilities when they brought in Talaria Technologies for around $20 million, their green energy capacity with the addition of Makani Power to the Google X team for around $30 million. They also brought in some great recruits with the purchase of DNNResearch headed up by Machine Learning pioneer Geoff Hinton for around $5 million, and employees from the venture fund Hatter again for around $5 million. Google also bought 217 patents from IBM during the first 9 months although it’s not clear where these have been accounted for and could be expensed in R&D.
This is a nominal selection of the 20 non-material acquisitions in the first three quarters of 2013, accounting for $318 million of the $369 million expenditure, but it helps establish a baseline for the cost of the robot companies they bought.
Yesterday Google posted their annual report, form 10K, on the Securities and Exchange Commission (SEC) website. In the annual report they confirm that during the twelve months ended 31st December 2013 they spent a total of $489 million on acquisitions that were not material and therefore did not require specific details. They also accounted for the Waze acquisition at $969 million – “The (Waze) acquisition is expected to enhance our customers’ user experience by offering real time traffic information to meet users’ daily navigation needs.”
Now we know that they spent $369 million on non-material companies in the first three quarters and $489 million during the whole twelve months we can safely calculate that $120 million was spent in the fourth quarter on non-material acquisitions.
The announcement of French company Flexycore for a rumored $23 million took place in the fourth quarter (end of October) and its not clear when they closed the deal for Flutter on whom they spent $40 million, as the media picked this up on 2nd October, but I’ll ‘assume’ they closed it in the 3rd quarter.
Taking the deal for Flexycore off the table indicates that Google spent an additional $97 million during the last quarter of 2013 on acquisitions, the time they bought the 8 robot companies
It’s likely that somewhere between $50 million and $90 million of the $1,458 billion Google spent on acquisitions in 2013 went on the purchase of those eight robot companies.
It’s fascinating that these acquisitions have created such media speculation given the nominal size of the purchase costs.
Of course this is very good news for the robotic industry as it possibly increases the value of the sector in the eyes of investors, raises awareness about the advances being made in robotics and brings an important subject about the future direction of technology and jobs into the public domain.
As I have said before, Google’s acquisition of robots and Artificial Intelligence technologies is anything but scary.
Photo: Andy Rubin Head of Google’s Robot Revolution, former head of Android
At the core of the early 21st century technology, with Internet connectivity and data driven by advances in Machine Learning; a sub-domain of what we call Artificial Intelligence, is integral to innovation advances.
A good definition of Artificial Intelligence (or maybe soft or logical AI), as provided by my friendly assistant, Google Now:
The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
Steve Jurvetson of DFJ said that he believes machine learning, a subset of AI will be one of the most important tech trends over the next 3-5 years for innovation and economic growth. By leveraging big data to allow computers to develop evolving behaviors, machine learning is vastly improving pattern recognition, allowing for broad application such as improved facial and speech recognition for application in many industries, especially national security.
Computer scientists have made significant advances in Machine Learning and soft AI with a particular set of approaches called “deep learning.” Deep Learning algorithms have been extremely successful for applications such as image recognition, speech recognition, and to some extent for natural language processing.
Deep Learning is the application of algorithms and software programming through ‘neural networks’ to develop machines, computers and robots that can do a wide variety of things including driving cars, working in factories, conversing with humans, translating speeches, recognizing and analyzing images and data patterns, and diagnosing complex operational or procedural problems.
One aspect of Deep Learning algorithms, which are also sometimes referred to as learning algorithms, which is receiving much work at major organizations, is providing a machine, computer or robot with the ability to learn from mostly unlabeled data, i.e. to work in a semi-supervised setting, where not all the examples come with complete and correct semantic labels. This was cleverly shown by Google with its ability to identify cats without labels on the photographs (Google builds a brain that can identify cats).
As Professor Yann Le Cunn, now at Facebook says:
The only way to build intelligent machines these days is to have them crunch lots of data — and build models of that data.
Sometimes it’s not who has the best algorithm that wins; it’s who has the most data.
Many Deep Learning scientists and academics are being recruited by Google, Facebook, Microsoft co-founder Paul Allen’s AI organization, Adobe, Amazon, Microsoft (see e.g. Bing), IBM to name a few.
Some of these recruits led the journalist and TV interviewer to quip: “The best minds of my generation are thinking about how to make people click ads.”
As witty (and sad) as that is, there is a degree of truth in it, however deep learning has a far more significant impact and many employers are seeking out people with deep learning capabilities.
Here are a just a few examples of how deep learning is improving how we use computers, wearable tech and robots.
Google Glass – New York Police Department are beta testing Google Glass programmed with Deep Learning. The officer wearing Glass will have access to a database for facial recognition, be able to record the event in real time. With respect to clearing up misunderstandings for law enforcement agents and citizens I see this as a very good move.
One of my favorite uses of Deep Learning can be seen in Amazon’s new Flow App. Flow recognizes items via their shape, size, color, box text, and general appearance. Hold your iPhone up to a row of items at a store, or in your home, and within seconds of “seeing” it with the iPhone’s camera, every recognizable item is placed in queue that can be added to your Amazon cart. You can use Flow to scan a row of competing products, then compare their prices and Amazon ratings once they land in your queue. Unsurprisingly, physical stores are not fans of this.
Deep Learning will be transformational in robotics. Nao, the companion robot created by Aldebaran Robotics, uses deep learning to improve its emotional intelligence, facial recognition and ability to communicate in multiple languages (see video below).
The real innovation challenge it seems will not be to apply deep learning to replace humans but to use it to create new ideas, products and industries that will continue to generate new jobs and opportunities.