- ‘Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey.
- The experimental vehicle, developed by researchers at the chip maker Nvidia, didn’t look different from other autonomous cars, but it was unlike anything demonstrated by Google, Tesla, or General Motors, and it showed the rising power of artificial intelligence.
- The car didn’t follow a single instruction provided by an engineer or programmer.
- Getting a car to drive this way was an impressive feat.
- But it’s also a bit unsettling, since it isn’t completely clear how the car makes its decisions.
‘Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey. The experimental vehicle, developed by researchers at the chip maker Nvidia, didn’t look different from other autonomous cars, but it was unlike anything demonstrated by Google, Tesla, or General Motors, and it showed the rising power of artificial intelligence. The car didn’t follow a single instruction provided by an engineer or programmer. Instead, it relied entirely on an algorithm that had taught itself to drive by watching a human do it.
Continue reading “The Dark Secret at the Heart of AI”
- Facebook Messenger users across the US are now being prompted to send and request money transfers by an artificial intelligence-based feature that detects when a payment is being discussed in a conversation on the social media platform and responds with a suggestion designed to help the user complete that payment.
- “M offers suggestions by popping into an open conversation to suggest relevant content and capabilities to enrich the way people communicate and get things done,” the social media giant says.
- M may make a suggestion in a conversation relevant to one of the core actions, and then the M logo and suggestion will appear — it’s that simple.”
- Facebook began testing payments through its Messenger service in July 2016.
- The social media giant also updated its Messenger chatbot platform to enable bots to accept payments without having to send shoppers to external sites to complete the checkout process in September 2016.
Facebook Messenger users across the US are now being prompted to send and request money transfers by an artificial intelligence-based feature.
Continue reading “Facebook Messenger adds payment prompts using artificial intelligence • NFC World”
- People figured that if they could find a way to codify instructions to a machine to tell it what steps to take, any manual operation could be eliminated saving any business time and money.
- Algorithms, on the other hand, are a series of steps that describe a way of solving a problem that meets the criteria of both being correct and ability to be terminated if need be.
- Instead of writing code to search our data given a set of parameters of the certain pattern as traditional coding focuses on, with big data we look for the pattern that matches the data.
- Now another step’s been added to the equation that finds patterns humans don’t see, such as the certain wavelength of light, or data over a certain volume.
- So, this new algorithmic step now successfully searches for patterns and will also create the code needed to do it.
We are all now in what’s called the “big data era,” and we’ve been here for quite some time. Once upon a time we were only just starting to piece together
Continue reading “Why Future Emphasis Should be on Algorithms”
- Ultimately, the approach could allow non-coders to simply describe an idea for a program and let the system build it, says Marc Brockschmidt, one of DeepCoder’s creators at Microsoft Research in Cambridge, UK.
- DeepCoder uses a technique called program synthesis: creating new programs by piecing together lines of code taken from existing software – just like a programmer might.
- “It could allow non-coders to simply describe an idea for a program and let the system build it”
One advantage of letting an AI loose in this way is that it can search more thoroughly and widely than a human coder, so could piece together source code in a way humans may not have thought of.
- DeepCoder created working programs in fractions of a second, whereas older systems take minutes to trial many different combinations of lines of code before piecing together something that can do the job.
- Brockschmidt says that future versions could make it very easy to build routine programs that scrape information from websites, or automatically categorise Facebook photos, for example, without human coders having to lift a finger
“The potential for automation that this kind of technology offers could really signify an enormous [reduction] in the amount of effort it takes to develop code,” says Solar-Lezama.
Software called DeepCoder has solved simple programming challenges by piecing together bits of borrowed code
Continue reading “AI learns to write its own code by stealing from other programs”
- Which is why it makes sense that Apple has announced it’s expanding its offices in Seattle, where much of its AI and machine learning work is done.
- Last August, Apple even bought a Seattle-based machine learning and artificial intelligence startup named Turi for an estimated $200 million, and the team is said to be moving into Apple’s offices at Two Union Square as part of the expansion.
- Carlos Guestrin, a University of Washington professor, former Turi CEO, and now director of machine learning at Apple, told GeekWire: “There’s a great opportunity for AI in Seattle.”
- He added: “We’re trying to find the best people who are excited about AI and machine learning — excited about research and thinking long term but also bringing those ideas into products that impact and delight our customers.”
- As part of the news, the University of Washington also announced a $1 million endowed professorship in AI and machine learning named after Guesterin.
In many ways, the tech world’s AI arms race is really a fight for talent. Skilled engineers are in short supply, and Silicon Valley’s biggest companies are competing to nab the best minds from…
Continue reading “Apple is expanding its Seattle offices to focus on AI and machine learning”
- TheTake, a site which launched as a way for consumers to buy that thing they saw in that movie, is set to begin selling an automated version of its service directly to businesses.
- The New York-based company is pitching studios and entertainment sites on a machine learning system that can identify products and locations as a way to generate revenue from product placements and experiential travel based on set locations.
- The new product is based on a year’s worth of work that TheTake’s development did to train a proprietary machine learning algorithm to identify images using a different technique than the industry standard, according to TheTake’s chief executive Ty the team behind TheTake would manually enter all the datasets and use an off-the-shelf computer visualization tool to identify images that fit the pre-defined parameters set by the company’s staff.
- While TheTake will continue to operate its service for consumers, which Cooper said has roughly half-a-million monthly users, it’s focus will shift to the business-to-business version of the service.
- Cooper, a former MGM Studios sales employee who managed partnerships with various cable companies from the studio’s New York office, launched the company with the help of a whiz kid from Columbia, Jared Browarnik, and consumer products developer from IAC and Amazon, Vincent Crossley.
TheTake, a site which launched as a way for consumers to buy that thing they saw in that movie, is set to begin selling an automated version of its service..
Continue reading “Working with major studios, TheTake launches AI image recognition engine for businesses”
- An AI can use Google Street View to help you decide where to move
- He plots 10,000 randomized points throughout a city, and grabs images taken by Google Street View.
- The idea of extracting information from Google Street View was inspired by MIT Media Lab’s StreetScore project, Keskkula writes, where machine learning was used to rank the safety of 3,000 streets in New York and Boston.
- Now one Estonia-based startup, Teleport , is using this idea, coupled with images from Google Street View, to automatically look around cities and see if people will like them based on their lifestyle preferences.
- Keskkula’s example focuses on motorcycles: He owns two and is interested in a city that welcomes them.
Machine learning is at its best when there’s way too much information for any human to comb through manually, like making high-volume stock trades or surfacing the best posts from hundreds of friends on Facebook. Now one Estonia-based startup, Teleport, is using this idea, coupled with images from Google Street View, to automatically look around cities and see if people will…
Continue reading “Artificial intelligence can look at Google Street View to help you decide where to move — Quartz”