We’re familiar with features like Siri or Microsoft’s Cortana which grope at a familiar concept from science fiction, yet leave us doing silly things like standing in public yowling at our phones. Amazon took a new approach to the idea of an artificial steward by cutting the AI free from our peripherals and making it an independent unit that acts in the household like any other appliance. Instead of steering your starship however, it can integrate with your devices via bluetooth to aide in tasks like writing shopping lists, or simply help you remember how many quarts are in a liter. Whatever you ask for, Echo will oblige.
The device is little more than the internet and a speaker stuffed into a minimal black cylinder the size of a vase, oh- and six far-field microphones aimed in each direction which listen to every word you say… always. As you’d expect, Echo only processes what you say after you call it to attention by speaking its given name. If you happen to be too far away for the directional microphones to hear, you can alternatively seek assistance from the Echo app on another device. Not bad for the freakishly low price Amazons asking, which is $100 for Prime subscribers. Even if you’re salivating over the idea of this chatting obelisk, or intrigued enough to buy one just to check it out (and pop its little seams), they’re only available to purchase through invite at the moment… the likes of which are said to go out in a few weeks.
The notion of the internet at large acting as an invisible ever-present swiss-army-knife of knowledge for the home is admittedly pretty sweet. It pulls on our wishful heartstrings for futuristic technology. The success of Echo as a first of its kind however relies on how seamlessly (and quickly) the artificial intelligence within it performs. If it can hold up, or prove to hold up in further iterations, it’s exciting to think what larger systems the technology could be integrated with in the near future… We might have our command center consciousness sooner than we thought.
With that said, inviting a little WiFi probe into your intimate living space to listen in on everything you do will take some getting over… your thoughts?
Continue reading “Echo, the First Useful Home Computer Intelligence?”
I’ve developed or have been involved with a number of imaging technologies, everything from DIY synthetic aperture radar, the MIT thru-wall radar, to the next generation of ultrasound imaging devices. Imagery is cool, but what the end-user often wants is some way by which to get an answer as opposed to viewing a reconstruction. So let’s figure that out.
We’re kicking-off a discussion on how to apply deep learning to more than just beating Jeopardy champions at their own game. We’d like to apply deep learning to hard data, to imagery. Is it possible to get the computer to accurately provide the diagnosis?
I helped to organize a seminar series/discussion panel in New York City on November 13th (you know, for those readers who are closer to New York than to Munich). This discussion panel includes David Ferrucci (the guy who lead the IBM Watson program), MIT Astrophysicist Max Tagmark, and the person who created genetic sequencing on a chip: Jonathan Rothberg. As the vanguard of creativity and enthusiasm in everything technical we’d like the Hackaday community to join the conversation.
Continue reading “Next Week in NYC: How the Age of Machine Consciousness is Transforming Our Lives”
We saw that some readers were not entirely happy with the team requirement for our Sci-Fi contest, which is running right now. We figured that those who do not work well with others might commit a bit of fraud to get around the requirement. But we’re delighted that someone found a much more creative solution. Why not enlist an AI to collaborate on your project?
[Colabot] is a hacker profile over on hackaday.io which is driven by ELIZA, a computer program that achieves limited interaction through natural language. Supposedly you add [Colabot] to your project and as it questions. We asked one on the profile page and are still awaiting the response. We think this itself could be a qualifying entry for the Sci-Fi contest if someone can find the right thematic spin to put on it.
As far as contest entries go there are only seven so far. Since everyone who submits an entry gets a T-shirt, and there are 15 total prize packages, we encourage you to post your entry as soon as possible. We want to see teams from hackerspaces and we can cryptically tell you that good things come to teams who post their project with the “sci-fi-contest” tag early!
Some thought the first artificial intelligence would come about as an accident, others as a war machine that decides the only way to protect humans is to kill them all. It turns out both these ideas were wrong. The first AI is apparently a teddy bear, available on Kickstarter for $60.
The Supertoy Kickstarter is selling a mechatronic teddy bear with motors, speakers, and enough electronics to connect to a cell phone. After plugging your cell phone and stuffing it in Teddy’s thorax, the bear comes alive with an intelligence all his own and a voice seemingly lifted from [Peter Griffin].
Needless to say, we’re just a bit skeptical that Teddy here can perform as demonstrated in the Kickstarter video. While the team behind Teddy has developed a successful talking chatbot before, the video makes this tech seem too good. Even the voice sounds like a real person with a microphone, and not like a clunky GPS personality.
Feel free to speculate in the comments on how good this tech can possibly be.
Ever since his daughter was born, [Markus] has been keeping logs full of observations of human behavior. Despite how it sounds, this sort of occurrence isn’t terribly odd; the field of developmental psychology is filled with research of this sort. It’s what [Markus] is doing with this data that makes his project unique. He’s attempting to use stochastic learning to model the behavior of his daughter and put her mind in a robot. Basically, [Markus] is building a robotic version of his newborn daughter.
The basics of stochastic learning (PDF with more info) is that a control system is modeled on an existing system – in this case, a baby – by telling a robot if it is doing a good or bad job. Think of it as classical conditioning for automatons that can only respond to a 1 or 0.
[Markus] built a robotic platform based on an Arduino Mega and a few ultrasonic distance sensors. By looking at its surrounding environment, the robot makes judgments as to what it should do next. In the video after the break, [Markus] shows off his robot finding its way around an obstacle course – really just a pair of couch cushions.
It’s a long way from crawling around on all fours, paying attention to shiny things, and making a complete mess of everything, but we’re loving [Markus]’ analytical approach to creating a rudimentary artificial intelligence.
Continue reading “Have a baby? Build another one!”
[Łukasz Kaiser] programmed a computer to play Tic-tac-toe. That doesn’t sound very remarkable until you realize he never told his computer the rules of Tic-tac-toe. The computer learned the rules by itself after watching a video of two people playing the game (link to actual paper – PDF warning).
[Łukasz] wrote a small program in C++ to recognize the placement of objects on a Tic-tac-toe, Connect 4, and Breakthough board. This program sifts through winning and losing games along with illegal moves to generate a Lambda calculus-like rule set for the relevant game. Even though [Łukasz] has only programmed a computer to learn simple games such as Tic-tac-toe, Connect 4, and Breakthrough, he plans to move up to more complex games such as Chess.
The fact that [Łukasz] programmed a computer to actually learn the rules of a game gives us pause; in one of the fabulous lectures [Richard Feynman] gave to freshman physics students in 1964, the subject of Chess came up. [Feynman] drew parallels between learning Chess and performing research. Every move is hypothesis testing, and when a very strange move occurs – castling, en passant, and the promotion of a pawn, for instance – the theory of the rules of the game must be reworked. Likewise, when extremely strange stuff happens in physics – particle/wave duality, and the existence of black holes – scientific theory is advanced.
Yes, teaching a computer to learn the rules of Tic-tac-toe may seem irrelevant, but given the same learning process can be applied to other fields such as medicine, economics, and just about every science, it’s not hard to see how cool [Łukasz]’ work is.
All of those orange, cyan, and yellow dots represent digital ants fighting for supremacy. This is a match to see who’s AI code is better in the Google backed programming competition: The AI Challenge. Before you go on to the next story, take a hard look at giving this a try for yourself. It’s set up as a way to get more people interested in AI programming, and they claim you can be up and running in just five minutes.
Possibly the best part of the AI Challenge is the resources they provide. The starter kits offer example code as a jumping off point in 22 different programming languages. And a quick start tutorial will help to get you thinking about the main components involved with Artificial Intelligence coding.
The game consists of ant hills for each team, water as an obstacle, and food collection as a goal. The winner is determined by who destroyed more enemy ant hills, and gathered more resources. It provides some interesting challenges, like how to search for food and enemy ant hills, how to plot a path from one point to another, etc. But if you’re interested in video game programming or robotics, the skills you learn in the process will be of great help later in your hacking exploits.