Lifeboat Foundation

Once you graduate beyond development boards like the Arduino or Wemos D1, you’ll find yourself in the market for a dedicated programmer. In most cases, your needs can be met with a cheap USB to serial adapter that’s not much bigger than a flash drive. The only downside is that you’ve got to manually wire it up to your microcontroller of choice.

Unless you’re [Roey Benamotz], that is. He’s recently created the LEan Mean Programming mAchine (LEMPA), an add-on board for the Raspberry Pi that includes all the sockets, jumpers, and indicator LEDs you need to successfully flash a whole suite of popular MCUs. What’s more, he’s written a Python tool that handles all the nuances of getting the firmware written out.

Continue reading “Turning The Raspberry Pi Into A MCU Programmer” »

The emergence of artificial intelligence and machine learning techniques is changing the world dramatically with novel applications such as internet of things, autonomous vehicles, real-time imaging processing and big data analytics in healthcare. In 2020, the global data volume is estimated to reach 44 Zettabytes, and it will continue to grow beyond the current capacity of computing and storage devices. At the same time, the related electricity consumption will increase 15 times by 2030, swallowing 8% of the global energy demand. Therefore, reducing energy consumption and increasing speed of information storage technology is in urgent need.

Berkeley researchers led by HKU President Professor Xiang Zhang when he was in Berkeley, in collaboration with Professor Aaron Lindenberg’s team at Stanford University, invented a new data storage method: They make odd numbered layers slide relative to even-number layers in tungsten ditelluride, which is only 3nm thick. The arrangement of these atomic layers represents 0 and 1 for data storage. These researchers creatively make use of quantum geometry: Berry curvature, to read information out. Therefore, this material platform works ideally for memory, with independent ‘write’ and ‘read’ operation. The energy consumption using this novel data storage method can be over 100 times less than the traditional method.

This work is a conceptual innovation for non-volatile storage types and can potentially bring technological revolution. For the first time, the researchers prove that two-dimensional semi-metals, going beyond traditional silicon material, can be used for information storage and reading. This work was published in the latest issue of the journal Nature Physics^[1]. Compared with the existing non-volatile (NVW) memory, this new material platform is expected to increase storage speed by two orders and decrease energy cost by three orders, and it can greatly facilitate the realization of emerging in-memory computing and neural network computing.

Continue reading “‘Berry Curvature’ Memory: Quantum Geometry Enables Information Storage in Metal” »

Back in July, OpenAI’s latest language model, GPT-3, dazzled with its ability to churn out paragraphs that look as if they could have been written by a human. People started showing off how GPT-3 could also autocomplete code or fill in blanks in spreadsheets.

In one example, Twitter employee Paul Katsen tweeted “the spreadsheet function to rule them all,” in which GPT-3 fills out columns by itself, pulling in data for US states: the population of Michigan is 10.3 million, Alaska became a state in 1906, and so on.

Except that GPT-3 can be a bit of a bullshitter. The population of Michigan has never been 10.3 million, and Alaska became a state in 1959.

Continue reading “This know-it-all AI learns by reading the entire web nonstop” »

When a patient climbs into an MRI scanner, it peers inside their body to reveal the complex anatomy within, like the ligaments and tendons in a knee.

DeepMind today announced a new milestone for its artificial intelligence agents trained to play the Blizzard Entertainment game StarCraft II. The Google-owned AI lab’s more sophisticated software, still called AlphaStar, is now grandmaster level in the real-time strategy game, capable of besting 99.8 percent of all human players in competition. The findings are to be published in a research paper in the scientific journal Nature.

Not only that, but DeepMind says it also evened the playing field when testing the new and improved AlphaStar against human opponents who opted into online competitions this past summer. For one, it trained AlphaStar to use all three of the game’s playable races, adding to the complexity of the game at the upper echelons of pro play. It also limited AlphaStar to only viewing the portion of the map a human would see and restricted the number of mouse clicks it could register to 22 non-duplicated actions every five seconds of play, to align it with standard human movement.

MIT’s Cheetah 3 robot can now leap and gallop across rough terrain, climb a staircase littered with debris, and quickly recover its balance when suddenly yanked or shoved, all while essentially blind.

Watch more videos from MIT: https://www.youtube.com/user/MITNewsOffice?sub_confirmation=1

Continue reading “Vision-free MIT Cheetah” »

Circa 2018

SPIDERS often make people jump but a bunch of clever scientists have managed to train one to jump on demand.

Researchers managed to teach the spider – nicknamed Kim – to jump from different heights and distances so they could film the arachnid’s super-springy movements.

Continue reading “Scientists train spider to jump on demand in bid to launch army of pest-fighting robots” »

November 2019 is a landmark month in the history of the future. That’s when humanoid robots that are indistinguishable from people start running amok in Los Angeles. Well, at least they do in the seminal sci-fi film “Blade Runner.” Thirty-seven years after its release, we don’t have murderous androids running around. But we do have androids like Hanson Robotics’ Sophia, and they could soon start working in jobs traditionally performed by people.

Russian start-up Promobot recently unveiled what it calls the world’s first autonomous android. It closely resembles a real person and can serve in a business capacity. Robo-C can be made to look like anyone, so it’s like an android clone. It comes with an artificial intelligence system that has more than 100,000 speech modules, according to the company. It can operate at home, acting as a companion robot and reading out the news or managing smart appliances — basically, an anthropomorphic smart speaker. It can also perform workplace tasks such as answering customer questions in places like offices, airports, banks and museums, while accepting payments and performing other functions.

Continue reading “Insanely humanlike androids have entered the workplace and soon may take your job” »

Artificial Intelligence (AI) is one of the most powerful technologies ever developed, but it’s not nearly as new as you might think. In fact, it’s undergone several evolutions since its inception in the 1950s. The first generation of AI was ‘descriptive analytics,’ which answers the question, “What happened?” The second, ‘diagnostic analytics,’ addresses, “Why did it happen?” The third and current generation is ‘predictive analytics,’ which answers the question, “Based on what has already happened, what could happen in the future?”

While predictive analytics can be very helpful and save time for data scientists, it is still fully dependent on historic data. Data scientists are therefore left helpless when faced with new, unknown scenarios. In order to have true “artificial intelligence,” we need machines that can “think” on their own, especially when faced with an unfamiliar situation. We need AI that can not just analyze the data it is shown, but express a “gut feeling” when something doesn’t add up. In short, we need AI that can mimic human intuition. Thankfully, we have it.