Category Archives: Artificial Intelligence

Teaching chores to an artificial agent

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For many people, household chores are a dreaded, inescapable part of life that we often put off or do with little care. But what if a robot assistant could help lighten the load?

Recently, computer scientists have been working on teaching machines to do a wider range of tasks around the house. In a new paper spearheaded by MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the University of Toronto, researchers demonstrate “VirtualHome,” a system that can simulate detailed household tasks and then have artificial “agents” execute them, opening up the possibility of one day teaching robots to do such tasks.

The team trained the system using nearly 3,000 programs of various activities, which are further broken down into subtasks for the computer to understand. A simple task like “making coffee,” for example, would also include the step “grabbing a cup.” The researchers demonstrated VirtualHome in a 3-D world inspired by the Sims video game.

The team’s artificial agent can execute 1,000 of these interactions in the Sims-style world, with eight different scenes including a living room, kitchen, dining room, bedroom, and home office.

“Describing actions as computer programs has the advantage of providing clear and unambiguous descriptions of all the steps needed to complete a task,” says MIT PhD student Xavier Puig, who was lead author on the paper. “These programs can instruct a robot or a virtual character, and can also be used as a representation for complex tasks with simpler actions.”

The project was co-developed by CSAIL and the University of Toronto alongside researchers from McGill University and the University of Ljubljana. It will be presented at the Computer Vision and Pattern Recognition (CVPR) conference, which takes place this month in Salt Lake City.

Unlike humans, robots need more explicit instructions to complete easy tasks; they can’t just infer and reason with ease.

For example, one might tell a human to “switch on the TV and watch it from the sofa.” Here, actions like “grab the remote control” and “sit/lie on sofa” have been omitted, since they’re part of the commonsense knowledge that humans have.

To better demonstrate these kinds of tasks to robots, the descriptions for actions needed to be much more detailed. To do so, the team first collected verbal descriptions of household activities, and then translated them into simple code. A program like this might include steps like: walk to the television, switch on the television, walk to the sofa, sit on the sofa, and watch television.

Once the programs were created, the team fed them to the VirtualHome 3-D simulator to be turned into videos. Then, a virtual agent would execute the tasks defined by the programs, whether it was watching television, placing a pot on the stove, or turning a toaster on and off.

The end result is not just a system for training robots to do chores, but also a large database of household tasks described using natural language. Companies like Amazon that are working to develop Alexa-like robotic systems at home could eventually use data like these to train their models to do more complex tasks.

The team’s model successfully demonstrated that their agents could learn to reconstruct a program, and therefore perform a task, given either a description: “pour milk into glass” or a video demonstration of the activity.

“This line of work could facilitate true robotic personal assistants in the future,” says Qiao Wang, a research assistant in arts, media, and engineering at Arizona State University. “Instead of each task programmed by the manufacturer, the robot can learn tasks just by listening to or watching the specific person it accompanies. This allows the robot to do tasks in a personalized way, or even some day invoke an emotional connection as a result of this personalized learning process.”

In the future, the team hopes to train the robots using actual videos instead of Sims-style simulation videos, which would enable a robot to learn simply by watching a YouTube video. The team is also working on implementing a reward-learning system in which the agent gets positive feedback when it does tasks correctly.

“You can imagine a setting where robots are assisting with chores at home and can eventually anticipate personalized wants and needs, or impending action,” says Puig. “This could be especially helpful as an assistive technology for the elderly, or those who may have limited mobility.”

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Artificial Intelligence in Formula 1 Strategy – Part 2/2

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In Artificial Intelligence in Formula 1 Strategy – Part 1 I discussed the motivation for using advanced artificial intelligence in Formula 1 strategy.

I looked at the scenarios of pit-stop timing, tyre choice and team orders to give a few examples as to what AI in F1 could focus on.

In this article I want to look at a few key areas Formula 1 needs to focus on to make the transition to more and better artificial intelligence applications for strategy:

  • Data
  • More Data
  • Even More Data
  • Machine Learning Models
  • Huge Investment in Compute Power


The teams are currently collecting a plethora of data from each test, qualifying and race. This “big data” will just grow and grow and grow. And the more teams learn about the data requirements for AI the more data they will undoubtably collect.

A few examples of data the teams have are:

  • Telemetry data from onboard sensors measuring anything from speeds, vibrations/frequencies, temperatures, pressures, etc.
  • Driver input to steering, acceleration, braking and of course the less numeric, but still vital, verbal communications.
  • Track data with times captured every 200m (give or take) + lap times, top speeds, pit stop times, track temperatures, wind speeds, etc.

A sample of Ferrari’s driver comparison telemetry data visualised.

I won’t go into more detail here as it is the given and obvious basis the sport is already operating on.

More Data

Of course, no matter how much data you can collect in the real world, there is always the opportunity to create useful data.

Not every race progress and outcome thinkable can be measured – given the limited amount of races per year.

However teams can create race simulations with realistic parameters and random influences to simulate millions of races. This simulation data can be used as “input” into machine/deep learning systems.

For example, imagine a “game like” simulation of every race on the calendar with many potential technical variations, weather variations, temperature variations, random crash and safety car insertions, etc. You suddenly have the chance to have enough data for an algorithm to learn what outcome was best in which scenario without ever having seen that actual race happen in real life.

 It cannot be too hard to get data like this from pure simulations. This is actual data from the Monaco race in 2011.

It cannot be too hard to get data like this from pure simulations. This is actual data from the Monaco race in 2011.

The impact of fictitious strategy decisions can then be observed and understood in quantitative ways with prediction abilities that give the team, sometimes maybe unexpected, strategy suggestions for any given race situation.

Compared to real world data this of course has the opportunity to become a much larger set of information.

I have a strong suspicion AI based systems will do an incredible job with this data given they have the right kind of machine learning models paired with them.

Even More Data

This is where the teams need to be innovative. As humans we can hear something is wrong with a competitor’s car when it drives by, we can judge how bad flat spots are visually in the TV slow-motion footage, we can judge tyre wear visually, we can “somewhat” hear things computers will not immediately be able to comprehend.

 Of course you won't expect to see images as clear as this, but you will see how bad a flat spot is ... paid that image recognition with other data ... e viola.

Of course you won’t expect to see images as clear as this, but you will see how bad a flat spot is … paid that image recognition with other data … e viola.

But, what stops Formula 1 teams from shooting super-high resolution, high speed, images or videos of cars driving by? Pairing that data with their own to judge vibration levels caused by tyre flat spots (usually created when a driver locks the breaks) should not be too hard either. Installing high fidelity microphones to record sounds in so much more detail than the human ear and brain can handle and using that data in correlation with their data to predict changes to a competitor’s car performance. Natural language input from other team’s radio communications. Visually measuring brake performance via relative deceleration comparisons and thermal vision. The list is endless, so many things could be done beyond what is already happening to collect more valuable and actionable data.

This is where F1 teams can gain an edge … by being more creative and clever than others.

Machine Learning

I won’t elaborate too much here. It is a given that a sufficient quantity and quality of input data is required to then feed into a new set of machine learning systems to start gaining insights and reap the benefits of AI.

F! teams will likely need to think of AI as a new department at least, with it’s own R&D, it’s own facilities and it’s own world-class staff.

An additional upside is that F1 teams can independently become leaders in artificial intelligence in general. Technology Groups like McLaren could start rivalling companies like IBM with a similar offering to Watson with the emphasis on strategic decision making in business.

Compute Power

Naturally all of this will require several orders of magnitude more storage space, computational requirements, communications speeds, etc.

But all of these problems can fairly easily be overcome these days. The cloud has vast storage and compute power to offer. And I bet it is not too much of an issue to set up a bespoke datacenter for these needs as well. If you can afford to run a wind-tunnel 24/7 you can afford what it takes to do AI the right way.

The biggest burden here is cost – how much is 1s per lap worth vs how much would it cost to get that advantage with AI is what it all boils down to.


It will happen, it just simply has to happen. In a game of diminishing returns on the hardware and aerodynamics engineering side of things, other avenues need to be explored to gain an advantage.

Artificial Intelligence with Machine & Deep Learning in Formula 1 Strategy will provide unknown and unexpected insights as well as highlight new areas to be explored for maximum gain.

Artificial Intelligence is one of the most glaringly obvious places where Formula 1 teams can make substantial competitive gains by:

  • Reducing or even eliminating human error in some areas.
  • Making better strategy decisions and predictions when little data may be available or processing is just not fast or conclusive enough.
  • Exploring completely new areas where AI can offer advantages.
  • Assuring drivers that the artificial intelligence is unbiased and will always work towards the maximum gain of first, and foremost, the team … and then the drivers.

This is a team sport and the money is awarded at team level after all … so the teams will have to move fast and significantly invest in AI before someone else does.

Read Part One

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Is Algorithmic Intelligence Different from Human Intelligence? 1 of 4

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Michael Burgstahler (Master of Neural Networks) wrote:

I’ll skip the equally hot debate about the definition of “intelligence” here and try to concentrate on the essence of this question…

Algorithmic intelligence is by definition very limited in scope, though extremely efficient within its scope. Whereas human intelligence, well… humans can cope with almost anything if you don’t rely on an optimal answer to a given question (pun intended).

Let me elaborate…

Algorithms are clearly defined steps about transforming well-defined input into desirable output. Therefore algorithms can be encoded into a machine (hardware or software) which can replicate those steps very efficiently.

The trick here is: for an algorithm, in order to operate efficiently (here meaning fast and focussed), it must not be a subject to transformations caused by itself. As soon as the algorithm can change its very nature, all bets are off as we can’t predict/optimize its way of working towards desired results anymore.

And that’s exactly what human intelligence is capable of: the human brain doesn’t work strictly algorithmic but constantly adapts its pathways to deal with unexpected changes of input. 

There isn’t a “program” or “operating system” working inside the human brain, no matter how many branch-conditions and variables you might define. The human brain is more of a giant pattern recognition engine (very simple abstraction!), constantly re-configuring its recognition and action triggers according to the input and the feedback about its actions’ outcomes.

So, to conclude …

If you want to transform data which can be logically and clearly defined into some output which is equally well defined, algorithms will always win hands down.

That includes simple machines as well as computer software. All these winners can be summarized as “Von Neumann machines“.

If you want to transform potentially ambiguous, illogical and unexpected data into somewhat desirable, adaptive but essentially unpredictable and not entirely reliable output, you need a human as your general purpose “machine”.

The Computer and the Brain: Abused City (The Silliman Memorial Lectures Series)

By John von Neumann

Theory of Games and Economic Behavior (Princeton Classic Editions) by John von Neumann (2004-05-30)

By John von Neumann;Oskar Morgenstern

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The Nature of Intelligence

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After my recent blog post someone who is rightfully concerned about the term “Artificial Intelligence”, and how it is colloquially used, commented on Facebook. This made me realise that I should look into the term more, and maybe dive a bit deeper into the meanings of words, their definitions, common use and perception over time. While this commenter suggested “Assisted Intelligence” as a better replacement, I think there are likely many more (most not very good) terms that should and could be explored on this blog. I did some searching and came across this (you should definitely read the full article): 

If you observe a newborn baby you will see that it is always in constant motion while awake. From the first day the baby tries to understand its environment. The movements of the limbs is really a baby questioning his world: how does this feel? It tries to move itself towards the feel good state.

I have always been interested in the subject of Artificial Intelligence. It is because by building AI we are learning valuable lessons about ourselves. After all, we consider us to be intelligent, but are not really sure what that means. AI is an attempt to reverse engineer our mind and to define intelligence by creating an abstracted version of it. Can AI become smarter than us? What is the true nature of intelligence? These are the questions that truly make me wonder.

Very recently there have been some astonishing AI advancements with models based on Deep Neural Networks (DNNs). Apparently today AI can perform image identification better than humans and win against the world champion of board game GO. We have lost tic-tac-toe, checkers and chess to a digital mind long time ago. These board games are closed environment discreet systems that have winning conditions and rules strictly defined. Older AIs used mostly brute-force and sheer computing power to win against humans. DNNs however are solving problems using evolved pattern recognition. This way they can tackle more fuzzy and human-like problems that have vast solution search space.

The holy grail of the field of AI is to develop the so-called seed intelligence or general purpose intelligence. A program that would exhibit such properties would be able to modify itself and perform general purpose tasks that have been given to it. In its versatility and adaptation it would be similar to human intelligence. Since such intelligence would be digital it might be many times more efficient than our meaty brains and could quickly modify itself to become much more advanced than homo sapiens.

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“Artificial Intelligence” was the Fake News of 2016

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Putting the ‘AI’ into FAIL

“Fake News” vexed the media classes greatly in 2016, but the tech world perfected the art long ago. With “the internet” no longer a credible vehicle for Silicon Valley’s wild fantasies and intellectual bullying of other industries – the internet clearly isn’t working for people – “AI” has taken its place. But almost everything you read about AI is Fake News. The AI coverage comes from a media willing itself into a mind of a three year old child, in order to be impressed.

For example, how many human jobs did AI replace in 2016? If you gave professional pundits a multiple choice question listing these three answers: 3 million, 300,000 and none, I suspect very few would choose the correct answer, which is of course “none”.

Similarly, if you asked tech experts which recent theoretical or technical breakthrough could account for the rise in coverage of AI, even fewer would be able to answer correctly that “there hasn’t been one”.

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2017 Will Be the Year of AI

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Looking back, 2016 feels to me like an unresolved mega-cap sumo wrestling match. The biggest companies in the industry squared off against each other, grunted a lot, made some feints and jabs—but failed to push each other out of the ring. Facebook cruised for much of the year and then got blamed for threatening all civil life as part of the fake news controversy. Apple inched backward, not so much from a competitive shove as from saturation in its biggest market, smartphones, and the absence of its next big thing. Amazon sparkled with the power of its web services arm and the sizzle of its Echo speaker. But its gains were evolutionary, not revolutionary. Google (Alphabet, if you must) looked after its costs, a somewhat unnatural act. Samsung wobbled mightily after its latest, greatest smartphone was recalled, but hasn’t yet fallen down. The biggest private companies, Uber, Airbnb, Snap(chat), and Pinterest, all matured—and stayed private.

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Is Algorithmic Intelligence Different from Human Intelligence? 2 of 4

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Ken Ewell (expertise in Deep Semantics and Conceptual Search Algorithms) writes:

The difference is in the space of functions or operations.

An intelligent computer algorithm (e.g. a binary decision procedure) runs (the functions of the intelligence operation; i.e. outputting a 1 or 0 based on input activity) in a well-managed virtual memory space under the strict control of the programming. The knowledge and intelligence of the authors are strictly replicated in the programming. The algorithm is not free to learn anything else.

Human intelligence is programmed in many ways, (e.g. cultural, religious influence, brainwashing) though it is ordinarily taught or trained by unwitting parents and teachers. Yet, unlike machines that strictly and involuntarily transform inputs into outputs, human intelligence need not depend on teachers or parents.

The precocious and playful child can learn anything with a willful aptitude for observing, grasping and discriminating the indisputable facts and relationships that give meaning to (make relevant) what is happening out of all that is taking place.

The willful aptitude appears to be a capacity (a well-developed mind). It is not an activity like intelligence. The mind appears to be a semantic (organizing) space for not only receiving information, also recognizing and triggering specified cognitive and emotional processes in light of inferred representations.

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Is Algorithmic Intelligence Different from Human Intelligence? 3 of 4

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Terrence Kwasha wrote:

Intelligence can be thought of as a classifier, as classification is a precursor to prediction which is a precursor to decision making.  You have to classify something as an instance of what you have seen before, so you can predict it will do something within the range of what you have seen that class of things do before.  

All known methods of algorithmic classification suffer from the problem of overfit.  Example: All training photos of a classroom have a blackboard, so a room with a whiteboard can’t be a classroom.  This is wrong.  Really the general concept of <Medium of communication> is a more useful for classification, but known algorithmic methods are not designed to create such generalizations.  Humans are.

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LinkedIn/eBay Founders Donating $20 Million to Protect Us from AI

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Reid Hoffman, the founder of LinkedIn, and the Omidyar Network, eBay founder Pierre Omidyar’s nonprofit, have each committed $10 million to fund academic research and development aimed at keeping artificial intelligence systems ethical and prevent building AI that may harm society.

The fund received an additional $5 million from the Knight Foundation and two other $1 million donations from the William and Flora Hewlett Foundation and Jim Pallotta, founder of the Raptor Group. The $27 million reserve is being anchored by MIT’s Media Lab and Harvard’s Berkman Klein Center for Internet and Society.

The Ethics and Governance of Artificial Intelligence Fund, the name of the fund, expects to grow as new funders continue to come on board.

One of the most critical challenges is how do we make sure that the machines we ‘train’ don’t perpetuate and amplify the same human biases that plague society.”

— Joi Ito, Director of MIT’s Media Lab

While AI has obvious benefits — it can be used to scan through troves of data to detect cancer and automate driving to reduce fatalities — a lot of very smart people, including the White House and Elon Musk’s nonprofit Open AI, have warned how artificially intelligent systems can go awry.

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I, Robot


By Isaac Asimov

I, Robot


Starring Will Smith, Bridget Moynahan, Bruce Greenwood, James Cromwell, Chi Mcbride

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Humans Will Be Able to Fall in Love with Computers Soon

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The world of the film Her is not as far away as you think.

— Ray Kurzweil

Flirting with a computer and even falling in love will be possible within just 15 years, a futurist has predicted.

The world depicted in the film Her, where a man develops a relationship with an intelligent computer operating system, is closer than we think, according to Google’s engineering director, Ray Kurzweil.

Speaking at the Exponential Finance conference in New York last week, he claimed technology will be capable of emotional interaction.

“My timeline is that computers will be at human level, such that you can have a relationship with them in 15 years from now – 2029,” he said.

“When I say about human levels, I’m talking about emotional intelligence. The ability to tell a joke, to be funny, to be romantic, to be loving, to be sexy, that is the cutting edge of human intelligence, that is not a sideshow.”

Mr Kurzweil claimed Her was a very realistic depiction of what can be achieved.

It may not be a comforting thought to people who have seen Joaquin Phoenix’s character’s soul-crushing attempt to escape his loneliness with Siri-like program voiced by Scarlett Johansson.

Credited with inventing the world’s first flat-bed scanners and text-to-speech synthesisers, Mr Kurzweil is perhaps most famous for his theory of “the singularity” – a point in the future where humans and machines will converge.

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Screenshots from the Movie “Her”






Set in the Los Angeles of the slight future, “Her” follows Theodore Twombly, a complex, soulful man who makes his living writing touching, personal letters for other people.



Starring Joaquin Phoenix, Amy Adams, Rooney Mara, Olivia Wilde, Scarlett Johansson

Her [DVD]


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