In what must have been a fun project, creative agency redpepper has built a robot which can play Where's Wally (Waldo in the US) using Google's AutoML Machine vision service.
We built a little robot called “There’s Waldo” to test the capabilities of Google’s new AutoML Vision service. We’ve found that technologies can be unapproac…
As more and more teams work with the Arcade Learning Environment to train RL models on Atari games, it becomes more important to a) ensure that comparisons are truly like-for-like and reproducible, and b) find ways to speed up and simplify model iteration. Google has developed a Tensorflow framework, called Dopamine, allowing researchers to focus on the content of their tests and code, rather than spending time iterating and tweaking.
Posted by Pablo Samuel Castro, Research Software Developer and Marc G. Bellemare, Research Scientist, Google Brain Team Reinforcement lear…
One problem with existing training methods for unsupervised learning used in human language translation is the availability of text "pairs" (such as the same sentence written twice) between obscure languages – such as Welsh to Urdu. Facebook is testing out a new mechanism involving "word embeddings" – vector-space representation of words in a given language (e.g. kitty -> cat is closer than kitty -> rocket). Using this model, the relative positions of words can be used to create the translation between languages for which there is little existing identical material. Although promising, performance is still lower than using supervised methods.
Automatic language translation is important to Facebook as a way to allow the billions of people who use our services to connect and communicate in their preferred language. To do this well, curren…
Looking for a way to pick up Python? Georgia Institute of Technology recently launched Code Shrew, a free, web-based learning platform for Python and object-oriented programming.
Shrew’d thinking: Code Shrew helps peeps who want to, or need to, gobble a slice of Py
A recently published poll shows that Americans are turning cold on one of the hottest technology frontiers – self-driving cars. A recent study showed that nearly half of drivers now say they'd "never" buy a Level 4 autonomous car, up from 30% two years ago, and Level 2 automation (which is recognised as being risky given the significantly increased cognitive effort it requires) is now the most popular option among those presented. The drop in enthusiasm for self-driving cars is not correlated with awareness of recent accidents involving the technology – but perhaps with a softening in hue of the rose tinted glasses we are using to project AI.
A new study from Cox Automotive shows that consumers now have a deeper understanding of the complexities involved when creating a self-driving car, which is causing them to reconsider their comfort…
Boston Dynamics has finally started to pivot from research towards manufacture of commercial products, with the following uses as priority:
– entertainment – the first and easiest scenario
– emergency response – eg search and rescue, surveillance
– Security – think about automating property line walks etc.
Longer term, they see their devices:
– warehouse logistics – although not clear why a wheeled robot isn’t a better fit
– parcel delivery – jumping from the back of a truck to deliver a parcel
– construction – perhaps an extension of emergency response, robots could carry heavy loads around sites
– caregiving – the ultimate scenario with the most complex requirements
But what are they going to do?
In a clear message to publishers (“We are not interested in talking to you about your traffic…That is the old world and there is no going back”), Facebook has reminded them both of the power it wields, and of their responsibilities – it’s not Facebook’s job to revive the business model of old school publishing, and deceptive practices like getting users to sign up for a monthly subscription without telling them up front the ongoing cost (https://rob.al/2OFsHQG), or making them call you on the phone during restricted hours to cancel (https://rob.al/2OESLuZ), seem like the desperate throws of a dying beast.
That firehose isn’t opening up again anytime soon.
At $10,000 for the top end card, NVIDA is clearly aiming these at professional video editing suites, but the addition of built in Tensor Core units aims to speed up end to end editing of film and game graphics – perhaps automating the removal of guide wires from shots, or removing artefacts or entire actual objects from scenes. I just wish the name (“Turing Architecture”) had been saved for a truly self-aware device 🙂
In recent days, word about Nvidia’s new Turing architecture started leaking out of the Santa Clara-based company’s headquarters. So it didn’t come as a major surprise that the company today announced…
AI models are complex and take time and compute power to generate – meaning they're expensive and valuable. IBM has been developing a way to "watermark" deep learning models by embedding specific information in to the model during training such that it's impossible (or very hard) to remove later, allowing definitive identification of model theft.
How can you tell if someone stole your AI models? IBM proposes a watermarking technique to protect AI developers and their intellectual property.
HBR's list of steps to making your AI projects more likely to be successful:
– ensure your purpose is clear. AI only adds value in the context of your business model and processes.
– chose carefully what you automate – the value is in expanding human effectiveness, not replacing humans entirely
– pick the right data – more data isnt necessarially better – you need the right data.
– finally, move people to higher value tasks – AI doesn't really reduce labour costs or headcount, it allows you to better use those people.
Start by having a clear sense of its goals.