Machines have been beating humans for years in games and contests: chess, Scrabble, checkers, Jeopardy! and more recently, the Chinese board game Go. Google’s AI machine won 4 out of 5 games in a $1.3 million contest against South Korean Lee Sedol, rated as the world’s best Go player. Sedol comments to ABC News at a post-game press conference:

I didn’t expect to lose. I didn’t think AlphaGo would play the game in such a perfect manner. Personally, I am regretful about the result, but would like to express my gratitude to everyone who supported and encouraged me throughout the match.

The 2,500-year-old board game is purported to be more complex than chess. It is played with black and white stones and the aim is to surround more territory than the opponent. Go appears to be a simple game and begins with an empty board. Two players (one using black stones, the other white), alternate placing stones in squares, trying to grab territory without getting their pieces captured.

According to Alan Levinovitz, writing in Wired a few years ago, “there are 400 possible board positions after the first round of moves in Chess and 129,960 in Go. There are 35 possible moves on any turn in a Chess game, and 250 for Go.” DeepMind’s David Silver and Demis Hassabis noted that the number of possible board configurations in Go is larger than the number of atoms in the universe.

Drake Baer, in a Tech Insider piece, describes DeepMind’s history and role in the development of AlphaGo:

DeepMind didn’t “program” AlphaGo with evaluations of  “good” and “bad” moves. Instead, AlphaGo’s algorithms studied a database of online Go matches, giving it the equivalent experience of doing nothing but playing Go for 80 years straight.

“This deep neural net is able to train and train and run forever on these thousands or millions of moves, to extract these patterns that leads to selection of good actions,” says Carnegie Mellon computer scientist Manuela Veloso, who studies agency in artificial intelligence systems.

Google acquired DeepMind in 2014. Founded in 2010 by chess prodigy-turned-artificial intelligence researcher Demis Hassabis, the company’s mission is to “solve intelligence,” and it claims that “the algorithms we build are capable of learning for themselves directly from raw experience or data.”

In February 2015, DeepMind revealed in Nature that the program learned to play vintage arcade games like Pong or Space Invaders as well as human players. Now it’s about to master a game that once seemed unmasterable for artificial intelligence [Go].

A Google DeepMind paper describes the technical details. You can also watch the Nature video below with DeepMind’s Demis Hassabis, who then describes the process required by the AI.

What does it all mean?

Go’s appeal lies in depth through simplicity, which is why its difficult for computers to master up — until now. There’s limited data available looking at the board. Choosing a good move demands a great deal of intuition. Until AlphaGo, no one had built an effective evaluation function. But AlphaGo’s use of deep learning and neural networks to teach itself enables it to process millions of Go positions and moves from human-played games, then deciding upon moves predicted on that experience.

Sam Byford writes in The Verge:

The twist is that DeepMind continually reinforces and improves the system’s ability by making it play millions of games against tweaked versions of itself. This trains a “policy” network to help AlphaGo predict the next moves, which in turn trains a “value” network to ascertain and evaluate those positions. AlphaGo looks ahead at possible moves and permutations, going through various eventualities before selecting the one it deems most likely to succeed. The combined neural nets save AlphaGo from doing excess work: the policy network helps reduce the breadth of moves to search, while the value network saves it from having to internally play out the entirety of each match to come to a conclusion.

Thus, AlphaGo gets better by playing itself. DeepMind believes that the principles it uses in AlphaGo have broader application and implications. Hassabis makes a distinction between “narrow” AIs like Deep Blue and artificial “general” intelligence (AGI), the latter being more flexible and adaptive. He thinks AlphaGo’s machine learning techniques will be useful in robotics, smartphone assistant systems, and healthcare.

Intuition has been the sole domain of humans. But now AlphaGo, Google, and DeepMind have shown they can build systems with an effective evaluation function to make choices based on a growing universe of experiential data. Reading medical scans and upgrading Siri (and other online resources and social assistants) are just the tip of the iceberg. This technology has implications for self-driving vehicles of all types (air, land and sea), medical diagnostics, and predictive stock picking to name just a few.

If you liked this article, you may also enjoy:

• Google DeepMind challenge match: Lee Sedol vs AlphaGo
• Talking Machines: Machine learning in healthcare and the AlphaGo matches

Google DeepMind Challenge Match: Lee Sedol vs AlphaGo. Credit: Deepmind/YouTube

In episode five of season two, Ryan walks us through variational inference. We put some listener questions about Go and how to play it to Andy Okun, president of the American Go Association (who is in Seoul, South Korea, watching the Lee Sedol/AlphaGo games). Plus, we hear from Suchi Saria of Johns Hopkins about applying machine learning to understanding health care data.

If you like this podcast, you may also be interested in:

• Talking Machines: AI safety and the legacy of Bletchley Park, with Nick Patterson
• Talking Machines: Robotics and machine learning music videos, with Michael Littman
• Talking Machines: Women in Machine Learning, with Hanna Wallach
• Talking Machines: Gaussian processes and OpenAI, with IIya Sutskever
• Talking Machines: Real human actions and Women in Machine Learning (WiML), with Jenn Wortman Vaughan

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

AlphaGo playing challenger Fan Hui. Source: Google DeepMind

Last week, media outlets reported about the latest ‘sure sign’ of an imminent robopocalypse. Google-backed DeepMind created an AI sophisticated enough to beat a human champion in the Chinese board game Go. AlphaGo defeated European champion Fan Hiu, surpassing IBM’s Deep Blue chess-playing victory over Gary Kasparov in 1997, marking another milestone of development and progress in AI research. Go is a more difficult game for humans to master – and it was thought also computers –  because of its complexity and need for players to recognise intricate patterns.

When I set off to write a note about this achievement I expected to find the usual sources in popular press with their characteristically subtle declarations, heralding the End of the Human Race is Nigh! However, thankfully, responses seem to be more sanguine and muted. The British tabloids even avoided using that picture of Terminator that invariably accompanies their reports on new developments in AI and robotics.

Perhaps this is a sign things are changing, and that the popular press are becoming more sensible and responsible in their technology reporting. Well, maybe lets see how many weeks – or days – we can go without this sort of thing before claiming victory, or remarking we’ve turned a significant corner.

From a cultural perspective, there is a lot of interest about DeepMind’s success, even if it hasn’t stirred the usual panic about the robopocalypse. It made me recall a conversation from an EURobotics event in Bristol in November. We humans like to think that we’re special. And maybe the possibility that robots or AI are a threat to that special status is another reason why we are so afraid of them. Maybe we fear another blow to our narcissism, like when that crazy astronomer Copernicus spoiled things by showing that Earth wasn’t the centre of the Universe. Or when Victorian poo-pooer Darwin demonstrated we evolved on Earth and weren’t placed here at the behest of some Divine Creator. Perhaps we don’t really fear that robots and AI will destroy all of humanity – well maybe we fear that, too – but perhaps part of the fear is that robots and AI will destroy another of those special things we reserve for ourselves as unique beings amidst creation.

Yet, our scientists aren’t going to let us sit wrapped in the warmth of our unique being. They keep pushing and developing more and more sophisticated AI that threatens our… specialness. How do we as a culture respond to this persistent challenge? Like any good politician, it seems we have decided to confront the inevitability of our failure by constantly changing the rules. Choose your sporting metaphor such as: we ‘move the goalposts‘ or we ‘raise the bar’.

Once upon a time it was enough to think of ourselves as the rational animal, the sole species on Earth bequeathed with the capacity for reason. Then, as evidence we could reason as the basis for unique status for humanity crumbled – both thanks to proof that animals were capable of sophisticated thought and the lack of proof that humans were rational – we shifted those goalposts. We transformed ourselves into the symbolic animal, the sole species on Earth endowed with the capacity to manipulate signs and represent.

We learned that whales, dolphins and other animals were communicating with each other all the time even if we weren’t listening. And that’s before we taught chimps how to use sign language (for which Charleton Heston will never thank us).

Then computers arrived to make things even worse. After some early experiments with hulking machines that struggled to add 2 + 2, computers eventually progressed to leave us in their wake. Computers can think more accurately and quickly than any human. They can solve complex mathematical equations therfore demonstrating they are pretty adept with symbols.

Ah, BUT…

Supercomputer Watson and human challengers on Jeopardy. Source: youtube

Humans could find solace in the comforting thought that computers were good at some things, yes, but they weren’t so smart. A computer could never beat a human at chess, for example. That is until May 1997 when chess champion Gary Kasparov lost to IBM’s Deep Blue.  But, that was always going to happen. A computer could never, we consoled ourselves, win at a game that required linguistic dexterity. Until 2011, when IBM’s Watson beat Ken Jennings and Brad Rutter at Jeopardy! the hit US game show. And now, Google’s DeepMind has conquered all, winning the most difficult strategy game we can imagine.

What is interesting about DeepMind’s victory is how human beings have responded again to the challenges of our self-conception posed by robots and AI. If we were under any illusion that we were special and alone among Gods’ creations as a thinking animal, a symbolising animal, or a playing animal, that status as been usurped by our own progeny again and again, in that all too familiar Greek-Frankenstein-Freudian way.

Animal rationabile had to give way to animal symbolicum, that in turn gave way to animal ludens. What’s left now for poor, biologically-limited humanity?

Lieutenant Commander Data in Star Trek. Source: youtube

A glimpse to this latest provocation can be seen in Star Trek: The Next Generation. Lieutenant Commander Data is a self-aware android with cognitive abilities far beyond any human being. Despite these tremendous capabilities, Data is always regarded – by himself and all the humans around him – as tragically, inevitably inferior, less than human. Despite lessons in Shakespeare and sermons in human romantic ideals from his mentor Captain Jean-Luc Picard, Data is doomed to be forever inferior to humans.

It seems now that AI can think and solve problems as well as humans, we’ve raised the bar again, changing the definition of ‘human’ to preserve our unique, privileged status.

We might now be animal permotionem – the emotional animal – except while that would be fine for distinguishing between us and robots at least until we upload the elusive ‘consciousness.dat’ file (as in Neill Blomkamp’s recent film, Chappie)  this new moniker won’t help us remain distinct from the rest of the animals. Because to be an emotional animal and a creature ruled by impulse and feeling is.. to just be an animal, according to all of our previous definitions.

We might find some refuge following Gene Roddenberry’s example in the notion of humans as unique animal artis: the animals that creates and engages in artistic work. The clever among you will have realised some time ago I’m not a classical scholar and my attempts to feign Latin fell apart some time ago. Artis seems to imply something more akin to ‘skill’, which robots arguably have already achieved; ars simply means ‘technique’ or ‘science’. Neither really capture what I’m trying to get at, suggestions are welcome.

The idea that human beings are defined by a particular creative impulse is not terribly new. Attempts to redefine ‘the human’ along these lines have been evident since the latter half of the twentieth century. For example if we flip back one hundred years, we might see Freud defining human beings (civilised human beings to clarify) as uniquely able to follow rules. But by the late 1960s, Freud’s descendants, like British psychoanalyst D. W. Winnicott, are arguing the exact opposite – what makes us human is creativity and the ability to fully participate in our being in an engaged, productive way.

What’s a poor AI to do? It was once enough for artificial intelligence to be sufficiently impressive maybe even deemed ‘human’ if it could prove capable of reason or symbolic representations, win at chess, Jeopardy!, or Go. Now we expect nothing less than Laurence Olivier, Lord Byron, and Jackson Pollack all in one.

https://www.youtube.com/watch?v=FcZ2NcCEC0o

This reminds me of Chris Columbus’s 1999 film Bicentennial Man (based on a story by Isaac Asimov). Robin Williams’s Andrew Martin begins his life, for lack of a better word, as a simple robot. Over the decades becomes more like a human and sentient, demonstrating artistic skill and feeling genuine emotion. At each stage he hopes to be recognised as a being on par with humans. No, he’s told, you’re not sentient. When he’s sentient he’s told he cannot feel. He’s told he cannot love. No achievement is enough.

Even once he has achieved everything and become like a human in every respect, perhaps even superhuman, he is told it is too much and has to be less. In a reversal of the Aristotelian notion of the thinking, superior animal Andrew is told to make mistakes. He is too perfect. He cannot be homo sapien, he needs to be homo errat, the man that screws up. It is not until Andrew is on his deathbed that the Speaker of the World Congress declares world will recognise Andrew as human.

Perhaps this will be the final line and definition of human that will endure every challenge posed by robots and artificial intelligence. No matter the level of technological progress, regardless how far artificial life leaves human beings behind: we will be homo mortuum. The rational animal that can die.

If Singularity enthusiasts and doomsayers are to be believed, this inevitable self-conception is not long off. Perhaps humans’ strength in our ability to adapt and the talent to re-invent ourselves, could mean there’s life in the old species yet. Regardless, it will serve us very well to create a conception of both ourselves and of artificial life forms that try to demarcate the boundaries and decide when these boundaries might be crossed. And what the implications will be for crossing that line.

This post originally appeared in Dreaming Robots.

Two years after the small London-based startup DeepMind published their pioneering work on “Playing Atari with Deep Reinforcement Learning”, they have become one of the leaders in the chase for Artificial General Intelligence. In short, their AI learns to play video games based only on the pixel values from the screen and the reward signal. The task is quite complicated because the system has to learn to recognize objects on the screen and figure out what the goal of the game is all by itself. A more thorough peek inside their technology can be found in our previous RoboHub article. In 2015 DeepMind made their source code available online, allowing anyone to implement and explore new scenarios for Deep Reinforcement Learning – the algorithm that combines deep neural networks and reinforcement learning to guide the actions of an AI agent.

In our research group we decided to study what happens when multiple AI agents are learning in the same environment. Multiagent systems are interesting because they allow a task to be distributed among several autonomous interacting agents, similar to when a goal is to be achieved by a team of people but without any boss or captain saying what each person should do. While the decentralized nature of multi-agent systems makes them robust, flexible and easily maintained, challenges emerge in any collaboration or competition between independent agents.

There are no guarantees that Deep Reinforcement Learning algorithms can provide meaningful results when two or more autonomous agents share an environment. The goal of an agent in a reinforcement learning system is to find a strategy (policy) that yields the most rewards possible. This comes down to learning which actions are the best in each situation. For a single agent in an unchanging environment the best action in a given situation is always the same. With more than one learning agent in the same environment, however, the world is in a constant state of change. Whenever one of the agents learns something new, the other agents must adapt to this new reality and change their behaviour accordingly. When they do so, the first agent must again change itself to adapt. This means the strategies may be in constant evolution, and it is not clear whether it’s even possible to define what the “best” strategy might be.

To study such scenarios, we modified DeepMind’s framework to allow two agents to share an environment and learn to either compete or collaborate. In our experiments we focused on the behaviour of the agents in well-known Pong game environment. First, we asked two AI agents to play the game against each other. Later, we changed how we rewarded the agents to promote collaboration instead of competition. We also explored the behaviour with rewarding schemes lying between competition and collaboration. To compare the emerging strategies we collected descriptions of their behaviour (e.g. how many times the players touch the ball before it goes out of play, how long they wait before serving the ball, etc …).

In the competitive mode, the two agents were rewarded for putting the ball past the opponent. As we let the agents play against each other, both of them got better and better with time. Eventually the agents learned to ward off even difficult fast-flying shots and send the ball back with speed. The behaviour at different stages of training and the final behaviour are illustrated in the video below;  the graph on the right describes how highly the agents themselves evaluate the current game situation (quality or Q-value) — the higher the Q-value, the more rewards the agent expects to receive in near future.

In the collaborative game mode both agents were penalized (received negative rewards) whenever the ball went out of play. These were the only rewards the agents received. Starting from a random behaviour, agents quickly learned that the best way to avoid losing the ball is to never launch the ball in the first place. When we nevertheless forced them to serve the ball, they developed a collaborative strategy where they could keep the ball alive indefinitely. To do so they positioned themselves directly facing each other and kept the ball flying in a straight line between themselves. Note that the agents were not explicitly told what the goal was, but generated  these strategies themselves as an effect of trying to avoid penalties. The learning process and the learned collaborative behaviour are illustrated by the video below:

The only difference between the above two cases was the reward an agent received for putting the ball past the opponent. In the competitive case this was rewarded positively, yielding +1 points; in the collaborative scheme this was rewarded negatively, with -1 points. We went on to explore what happens if this reward is given different values between -1 and +1. The results show that as we gradually increased the reward, the changes in behavioural descriptors were smooth. The transition from competition to collaboration seems to happen without abrupt changes.

This preliminary study of multi-agent systems using DeepMind’s AI leaves many interesting questions untouched. How rapidly can an agent switch from collaborative to competitive behaviour? What happens if one agent tries to compete and the other collaborate? Do agents learn faster when they play against an already proficient opponent (student and teacher)? How will our agents learn to behave if asked to compete or collaborate with a human player?

The future will show whether AI and humans can learn to collaborate or are destined to compete. In any case it is clear that the AI-s will always try that maximize their rewards – it is therefore our responsibility to define these rewards ethically and clearly enough to avoid costly surprises.

Our multi-agent version of DeepMind’s code and the necessary installation instructions are made public on Github and research paper is available on Arxiv. A Python implementation of the DeepMind algorithm can be found here. You can also find couple of more technical blog post on our website at Computational Neuroscience Lab in University of Tartu, Estonia.

If you liked this article, you may also be interested in:

• Artificial General Intelligence that plays Atari video games: How did DeepMind do it?
• Why robots will not be smarter than humans by 2029
• The Master Algorithm, with UW’s Pedro Domingos
• On ‘solving intelligence’, with DeepMind’s Nando de Freitas
• The economic impact of machine learning, with Baidu’s Andrew Ng

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Ryan introduces us to anchor words and how they can help us expand our ability to explore topic models. Plus, we take a question about the fundamentals of tackling a problem with machine learning.

In January, 2014, Google acquired London-based DeepMind Technologies for $643 million. Now it is adding to that purchase with two more companies, ten new hires and a substantial contribution to Oxford University.

Google DeepMind was a British AI start-up with some high-profile investors and a whiz kid founder (Demis Hassabis), and their forte was smart recommendations for online commerce. Now they are acquiring companies, talent and university research resources.

The two acquisitions are Dark Blue Labs (which is creating systems to understand natural language that would allow computers to comprehend the meaning of sentences and how people react to them) and Vision Factory (which is developing systems capable of the visual recognition of objects in the real world; this means, for example, giving robots three-dimensional awareness that can allow them to understand how a cup sits on a table). Both are UK-based, deep learning startups, and both were spun off from Oxford University. According to the Financial Times, the two acquisitions are estimated to have cost Google DeepMind $50 million.

The Google-Oxford partnership will cost more. A substantial contribution to Oxford University is forthcoming to expand Google’s AI and deep learning capabilities.

“Google DeepMind has hired all seven founders of these startups with the three professors holding joint appointments at Oxford University where they will continue to spend part of their time,” Hassabis notes in his blog post. “These exciting partnerships underline how committed Google DeepMind is to supporting the development of UK academia and the growth of strong scientific research labs.”

f you liked this article, you may also be interested in:

• Artificial General Intelligence that plays Atari video games: How did DeepMind do it?
• Google’s robot and artificial intelligence acquisitions are anything but scary
• Google’s DeepMind acquisition in reinforcement learning
• Big Deals Series: What does it mean to have the giants investing in robotics?

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Last December, an article named “Playing Atari with Deep Reinforcement Learning” was uploaded to arXiv by employees of a small AI company called DeepMind. Two months later Google bought DeepMind for 500 million euros, and this article is almost the only thing we know about the company . Currently our team is trying to replicate their artificial mind, and in this post we describe its inner workings.

The paper describes a single system that learns to play several old video games by selecting actions based on only the screen images and the reward signal (whether the score in the game is increased or not).

At first (before training), the program does not know anything about the game: it doesn’t know the objective of the game (if it should just stay alive, kill something or solve a puzzle), it has no understanding about its influence on the game (the consequences of its actions) and one could even say that it cannot see the objects in the game. By trial and error the system gradually learns how it should behave to receive a reward. Also note that the system uses the same architecture for all different games without any game-specific hints from the programmers other than how many possible actions there are in the game, e.g. left, right, up, down, space (fire) etc.

The reported results show that the system was able to master several games and play some of them better than a human player. This result can be seen as a step towards artificial general intelligence (AGI), which is really fascinating. Let us see in more detail how this was achieved.

The Task
The system receives a picture of the game screen (this example image is from the simplest game, Breakout) and after analyzing it, it has to choose an action to take. The action is executed and the system is told whether the score increased, decreased or did not change. Based on this information and playing a large number of games, the system needs to learn to improve its performance in the game.

Take a pause and think about it: try to outline in your head how you would approach this task if someone had asked you to program it. Keep in mind that you do not know in advance which game your system will need to play.

Machine Learning and Artificial Neural Networks
Before we dive into the implementation DeepMind came up with, let’s introduce the concepts of machine learning and artificial neural networks.

A very common task in machine learning is: given the information about an object decide which category it belongs to. In the case of DeepMind we want to decide which action to take given the current state of the game. Machine learning algorithms learn from examples: given many example objects and their categories, the algorithm figures out which features of the object are useful for identifying the category. The algorithm produces a model that minimizes misclassification rate in the training set. This model can later be used to predict the category of an unknown object.

Artificial Neural Networks (ANN) is one of the machine learning algorithms. It is inspired by the architecture of our brain. The network consists of nodes (as our brain consists of neurons) that are connected to each other (as neurons in our brain are connected using axons, dendrites and synapses). For each neuron we must specify the condition under which the neuron should transmit the signal forward (emit a spike in real neurons). By changing the strengths of the connections one can make the network perform computation. Modern architectures of neural networks usually consist of:

• Input layer of neurons (they receive the description of an object)
• Hidden layers (the main part, where the learning happens)
• Output layer (it has one neuron for each category, the category with highest activated node is the predicted class)

After the learning is done we can feed new objects to the network and see scores for each category in the output layer.

DeepMind

In this system an ANN is used to assign an expected reward to each of the possible actions given the current game state. The network can answer a question, e.g. “How good would it be to take this action now?”. The input to the network at any time point consists of the last four game screens, so that system is given not only the last position, but also a bit of information about how the situation in the game has evolved recently. This input is then passed through three successive hidden layers of neurons and finally to the output layer of the ANN.

The output layer has one node for each possible action and the activation of those nodes indicates the expected reward from each of them – here, the action with the highest expected reward (the “best” action) is selected for execution. But how does the network know how to calculate the expected scores in a good and correct way?

The Learning Process

After you have defined the structure of your network there is only one thing you can still change: the strength of the connections. The learning process is, therefore, just changing the weights in a way that for all of the training examples the network is capable to propose actions that lead to positive rewards.

It is an optimisation problem where the goal is to maximize the reward over time. The problem is solved by combining gradient descent with a simple idea related to reinforcement learning: the network attempts to maximize not only the immediate reward, but also the cumulative reward of future actions. This can be achieved by estimating the expected value of the next screen image using its current neural network. To put it another way, traditionally you would use (received reward – estimated reward) as the error signal for gradient descent, but in this case we also take in consideration the reward estimate from the next image.

Summary

To summarize all the pieces we have seen, let us have one final look:

1. Build a network and initialize connections to random values

2. Feed lots and lots of game situations into the network

3. Network proposes the action to take in each of them

4. If the action was good, we reward the system, if it was bad we penalize it

5. System updates its knowledge by updating the weights

6. After several hundreds of games the system outperforms a human player (in the case of Breakout)

This result can be seen as a small step forward from the traditional machine learning in the direction of artificial general intelligence. It might be a very small step (one might say that the system does not “know” or “understand” what it is doing), but the learning capabilities of DeepMind’s system are far larger than its predecessors’  – and the range of problems is can solve without specific hints from the programmers is much wider.

Replicate and Make It Open Source

At the moment we are trying to replicate this successful project and make it available to the whole scientific community for further research. The project is open source and you can jump on board to help on Github!

References

The original paper:
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. Playing atari with deep reinforcement learning.

Our implementation at GitHub:
Reproducing the results of “Playing Atari with Deep Reinforcement Learning” by DeepMind.

The authors of this article are from the Computational Neuroscience Group at University of Tartu’s Institute of Computer Science.

If you liked this article, you may also be interested in:

• Google’s robot and artificial intelligence acquisitions are anything but scary
• Google’s DeepMind acquisition in reinforcement learning
• On internal models, consequence engines and Popperian creatures
• Why robots will not be smarter than humans by 2029

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

If the world outside the robotics community didn’t know about Boston Dynamics, Kiva and Nest, they do now. Recent robotics acquisitions and investments by major-league players like Google, Apple and Amazon have generated a blitz of headlines in the robotics world and beyond. Are we witnessing a power play in the making? What does it mean for the future of robotics? And is all the hype beneficial or harmful to the robotics community?

This month’s Robotics by Invitation will serve as a launch for Robohub’s newest focus series on how big time corporate attention effects the culture of robotics. In the coming weeks we will be bringing you insight from the likes of Steve Cousins, Dan Kara, Valery Komissarova, Avner Levin, Chad Partridge, Gill Pratt, Erin Rapacki, Frank Tobe, and Rob Wilson.

Today, RBI panelists Mark Stephen Meadows, AJung Moon and Alan Winfield weigh in …

Alan Winfield on “What does it mean to have giants like Google, Apple and Amazon investing in robotics?”

Judging by the levels of media coverage and frenzied speculation that has followed each acquisition, the short answer to what does it mean is: endless press exposure. I almost wrote ‘priceless exposure’ but then these are companies with very deep pockets; nevertheless the advertising value equivalent must be very high indeed. The coverage really illustrates the fact that these companies have achieved celebrity status …

Read more →

Mark Stephen Meadows on “What does it mean to have giants like Google, Apple and Amazon investing in robotics?”

Google, is the wild card for me.  With more acquisitions than Apple, Amazon, Facebook, and Microsoft combined, the GOOG looks to be rigging up a kit that would offer excellent image recognition + navigation + mobility.   A robot that can roam around your home or maybe your city, and offer a helpful service. My bet is that these Googlebots (perhaps they will be cars) will have sharp eyes, simple yet helpful demeanors, and we’ll trade that robotic service for our personal info …

Read more →

AJung Moon on “What does it mean to have giants like Google, Apple and Amazon investing in robotics?”

It’s exciting for the robotics community that the giants (Google, Apple, and Amazon) are actively investing in robotics. Large scale investments by giant corporations, regardless of what their actual plans are, can have a positive impact on the job market for roboticists, increase competition between the giants and push robotics forward. But I began to feel uneasy when Google acquired Boston Dynamics …

Read more →

If you liked this post, you may also be interested in:

• Robohub focus: Dealing with the ‘jobs’ question
• Robohub focus on frontier robotics: High risks, high rewards
• Robotics by Invitation: Is open source a good business model for robotics?

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

It’s exciting for the robotics community that the giants (Google, Apple, and Amazon) are actively investing in robotics.

Indeed, my initial response to hearing about Google’s first seven of a series of acquisitions of robotics-related companies was a very positive one (these companies include Shaft, Industrial Perception, Meka Robotics, Redwood Robotics, Bot & Dolly, Autofuss, and Holomni, see Frank Tobe’s article for more details on these companies). Technologies from these companies are ones that can make robots mobile, perceptive, responsive, and interactive. The fact that the companies are now housed under one giant roof of Google made me imagine a future of next generation robots where Google-branded humanoids will be the next Baxter, working shoulder-to-shoulder with people in dynamic manufacturing environments, or assisting caregivers and serving seniors at care facilities. How could it not be exciting?

Even if Google’s actual direction is to put bipedal robots in self-driving cars so that people can enjoy fast, fully-autonomous door-to-door delivery of flowers on Valentine’s Day, my view was that such investments can only be good for the robotics community. Large scale investments by giant corporations, regardless of what their actual plans are, can have a positive impact on the job market for roboticists, increase competition between the giants and push robotics forward.

Then I began to feel a little bit uneasy when Google acquired Boston Dynamics. That’s because Boston Dynamics is not only a company with one of the most impressive control systems I’ve ever seen (I’m sure many of you will agree with this from the video of Boston Dynamic’s Big Dog, which has over 15 million views on YouTube), but it’s also a company that is known to have developed such impressive systems for the Pentagon, of all clients.

What does this all mean? What is Google planning to do with robots that can keep itself upright after being kicked, walk through rough terrains, run as fast as 29 miles per hour? The combination of Google-acquired companies became even more mysterious when the London-based AI company, DeepMind, was added to the mix. It’s safe to say that the uncertainty over Google’s plans with these companies has stirred up quite a bit of concern to the general public: some media coverage on the topic have been hyping the idea of Google building Skynet or being in the business of building robot soldiers.

Unless Google is indeed headed in those directions, I don’t believe that the public concern about the possibility of human extinction by Google-made robots in the near future is a good thing, either for the company or for the field of robotics in general. I think that public concerns over Google’s investment directions exist today because we, as a society, lack the presence of governing bodies whose role is to ensure that robotics technologies are developed and used for purposes desirable for the society, in manners aligned with society’s values.

However, I do applaud the fact that Google is setting up an ethics board to presumably oversee what they will and will not do with their technologies. If Google takes ethics seriously, I think Google could be a game changer in robotics (although it already is), not just by producing and selling cool robots in the future, but more importantly, by leading the way of tightly coupling ethics in robotics and making robots that “don’t do evil”.

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