You only need two simple letters to accurately convey the major shift in the technology space this year: A and I. Beyond those letters, however, is a complex, evolving and exciting way in which we work, communicate and collaborate. As you will see, artificial intelligence is a common thread as we embark on Microsoft Build, our annual flagship event for developers.
High-frequency light is useful. The higher the frequency of light, the shorter its wavelength—and the shorter the wavelength, the smaller the objects and details the light can be used to see.
So violet light can show you smaller details than red light, for example, because it has a shorter wavelength. But to see really, really small things—down to the scale of billionths of a meter, thousands of times less than the width of a human hair—to see those things, you need extreme ultraviolet light (and a good microscope).
Extreme ultraviolet light, with wavelengths between 10 and 120 nanometers, has many applications in medical imaging, studying biological objects, and deciphering the fine details of computer chips during their manufacture. However, producing small and affordable sources of this light has been very challenging.
New model for human evolution suggests Homo sapiens arose from multiple closely related populations.
A new study in Nature challenges prevailing theories, suggesting that Homo sapiens evolved from multiple diverse populations across Africa, with the earliest detectable split occurring 120,000–135,000 years ago, after prolonged periods of genetic intermixing.
In testing the genetic material of current populations in Africa and comparing it against existing fossil evidence of early Homo sapiens populations there, researchers have uncovered a new model of human evolution — overturning previous beliefs that a single African population gave rise to all humans. The new research was published on May 17, in the journal Nature.
Our guest is Nita Farahany, a Distinguished Professor at Duke University where she heads the Science, Law, and Policy Lab. The research she conducts in her lab specifically focuses on the implications of emerging neuroscience, genomics, and artificial intelligence; and, as a testament to her expertise, there is a long, long list of awards and influential positions she can lay claim to, including an appointment by Obama to the Presidential Commission for the Study of Bioethical Issues.
In this episode, we explore Nita’s recent publication, provocatively entitled, The Battle for Your Brain: Defending the Right to Think Freely in the Age of Neurotechnology. This takes us on our tour of the current neurotechnology that exists, the upcoming ways in which this tech will be integrated into our daily products, how it will shape our decision making, the profound list of ethical considerations surrounding cognitive liberty, and much more.
Continue reading “Thinking Freely in the Age of Neurotechnology | Nita Farahany, ep102” »
Artificial intelligence is a superior lifeform that humans are creating, and many AI researchers have outlined various scenarios in which this technology can pose an existential risk to humanity that could result in the literal end of the world.
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Engineers in Japan just shattered the world record for the fastest internet speed, achieving a data transmission rate of 319 Terabits per second (Tb/s), according to a paper presented at the International Conference on Optical Fiber Communications in June. The new record was made on a line of fibers more than 1,864 miles (3,000 km) long. And, crucially, it is compatible with modern-day cable infrastructure.
A team of researchers at Duke University and their collaborators have uncovered the atomic mechanisms that make a class of compounds called argyrodites attractive candidates for both solid-state battery electrolytes and thermoelectric energy converters.
The discoveries—and the machine learning approach used to make them—could help usher in a new era of energy storage for applications such as household battery walls and fast-charging electric vehicles.
The results appeared online May 18 in the journal Nature Materials.
May 22 (Reuters) — Nvidia Corp (NVDA.O) on Monday said it has worked with the U.K.’s University of Bristol to build a new supercomputer using a new Nvidia chip that would compete with Intel Corp (INTC.O) and Advanced Micro Devices Inc (AMD.O).
Nvidia is the world’s top maker of graphics processing units (GPUs), which are in high demand because they can be used to speed up artificial intelligence work. OpenAI’s ChatGPT, for example, was created with thousands of Nvidia GPUs.
But Nvidia’s GPU chips are typically paired with what is called a central processing unit (CPU), a market that has been dominated by Intel and AMD for decades. This year, Nvidia has started shipping its own competing CPU chip called Grace, which is based on technology from SoftBank Group Corp-owned (9984.T) Arm Ltd.
Errors are the Achilles’ heel of quantum computation, cropping up at random and threatening to rui.
Meta has created an AI language model that (in a refreshing change of pace) isn’t a ChatGPT clone. The company’s Massively Multilingual Speech (MMS) project can recognize over 4,000 spoken languages and produce speech (text-to-speech) in over 1,100. Like most of its other publicly announced AI projects, Meta is open-sourcing MMS today to help preserve language diversity and encourage researchers to build on its foundation. “Today, we are publicly sharing our models and code so that others in the research community can build upon our work,” the company wrote. “Through this work, we hope to make a small contribution to preserve the incredible language diversity of the world.”
Speech recognition and text-to-speech models typically require training on thousands of hours of audio with accompanying transcription labels. (Labels are crucial to machine learning, allowing the algorithms to correctly categorize and “understand” the data.) But for languages that aren’t widely used in industrialized nations — many of which are in danger of disappearing in the coming decades — “this data simply does not exist,” as Meta puts it.
Meta used an unconventional approach to collecting audio data: tapping into audio recordings of translated religious texts. “We turned to religious texts, such as the Bible, that have been translated in many different languages and whose translations have been widely studied for text-based language translation research,” the company said. “These translations have publicly available audio recordings of people reading these texts in different languages.” Incorporating the unlabeled recordings of the Bible and similar texts, Meta’s researchers increased the model’s available languages to over 4,000.
