Reinforcement learning algorithms are only now starting to be applied in business settings — but could help companies solve complicated problems. Here’s how.
Most machine learning systems leverage historical data to make predictions. But learning through trial and error lead to more creative solutions.
Researchers at the National Institute of Standards and Technology are proposing a new approach to large-scale artificial intelligence (AI) by relying on the integration of photonic components with superconducting electronics.
Previous approaches to achieving general intelligence in artificial intelligence systems have focused on conventional silicon microelectronics paired with light. There are major barriers to this approach, however. There are many physical and practical limitations with the fabrication of silicon chips with electronic and photonic elements.
General intelligence is “the ability to assimilate knowledge across content categories and to use that information to form a coherent representation of the world.” It involves the integration of various sources of information, and it must result in a coherent and adaptive model of the world. The design and hardware construction for general intelligence requires the application of principles of neuroscience and very-large-scale integration.
As content moderation continues to be a critical aspect of how social media platforms work — one that they may be pressured to get right, or at least do better in tackling — a startup that has built a set of data and image models to help with that, along with any other tasks that require automatically detecting objects or text, is announcing a big round of funding.
Hive, which has built a training data trove based on crowdsourced contributions from some 2 million people globally, which then powers a set of APIs that can be used to identify automatically images of objects, words and phrases — a process used not just in content moderation platforms, but also in building algorithms for autonomous systems, back-office data processing, and more — has raised $85 million in a Series D round of funding that the startup has confirmed values it at $2 billion.
“At the heart of what we’re doing is building AI models that can help automate work that used to be manual,” said Kevin Guo, Hive’s co-founder and CEO. “We’ve heard about RPA and other workflow automation, and that is important too but what that has also established is that there are certain things that humans should not have to do that is very structural, but those systems can’t actually address a lot of other work that is unstructured.” Hive’s models help bring structure to that other work, and Guo claims they provide “near human level accuracy.”
Cerebras Systems has unveiled its new Wafer Scale Engine 2 processor with a record-setting 2.6 trillion transistors and 850000 AI-optimized cores. It’s built for supercomputing tasks, and it’s the second time since 2019 that Los Altos, California-based Cerebras has unveiled a chip that is basically an entire wafer.
Chipmakers normally slice a wafer from a 12-inch-diameter ingot of silicon to process in a chip factory. Once processed, the wafer is sliced into hundreds of separate chips that can be used in electronic hardware.
But Cerebras, started by SeaMicro founder Andrew Feldman, takes that wafer and makes a single, massive chip out of it. Each piece of the chip, dubbed a core, is interconnected in a sophisticated way to other cores. The interconnections are designed to keep all the cores functioning at high speeds so the transistors can work together as one.
AI systems can lead to race or gender discrimination.
The US Federal Trade Commission has warned companies against using biased artificial intelligence, saying they may break consumer protection laws. A new blog post notes that AI tools can reflect “troubling” racial and gender biases. If those tools are applied in areas like housing or employment, falsely advertised as unbiased, or trained on data that is gathered deceptively, the agency says it could intervene.
“In a rush to embrace new technology, be careful not to overpromise what your algorithm can deliver,” writes FTC attorney Elisa Jillson — particularly when promising decisions that don’t reflect racial or gender bias. “The result may be deception, discrimination — and an FTC law enforcement action.”
As Protocol points out, FTC chair Rebecca Slaughter recently called algorithm-based bias “an economic justice issue.” Slaughter and Jillson both mention that companies could be prosecuted under the Equal Credit Opportunity Act or the Fair Credit Reporting Act for biased and unfair AI-powered decisions, and unfair and deceptive practices could also fall under Section 5 of the FTC Act.
Someday, scientists believe, tiny DNA-based robots and other nanodevices will deliver medicine inside our bodies, detect the presence of deadly pathogens, and help manufacture increasingly smaller electronics.
Researchers took a big step toward that future by developing a new tool that can design much more complex DNA robots and nanodevices than were ever possible before in a fraction of the time.
In a paper published on April 19, 2021, in the journal Nature Materials, researchers from The Ohio State University – led by former engineering doctoral student Chao-Min Huang – unveiled new software they call MagicDNA.
The Art Of Human Care For Covid-19 — Dr. Hassan A. Tetteh MD, Health Mission Chief, U.S. Department of Defense (DoD), Joint Artificial Intelligence Center, The Pentagon.
Dr. Hassan A. Tetteh, MD, is the Health Mission Chief, at the Department of Defense (DoD) Joint Artificial Intelligence Center, serving to advance the objectives of the DoD AI Strategy, and improve war fighter healthcare and readiness with artificial intelligence implementations.
Dr. Tetteh is also an Associate Professor of Surgery at the Uniformed Services University of the Health Sciences, adjunct faculty at Howard University College of Medicine, a Thoracic Staff Surgeon for MedStar Health and Walter Reed National Military Medical Center, and leads a Specialized Thoracic Adapted Recovery (STAR) Team, in Washington, DC, where his research in thoracic transplantation aims to expand heart and lung recovery and save lives.
OpenAI says its text-generating system GPT-3 is now being used by more than 300 companies and tens of thousands of developers, who are collectively generating more than 4.5 billion words a day. It’s an arbitrary milestone, but a clear example of the potential for AI text-generation.
Circa 2020
Robots and stranger machines have been using a particular band of ultraviolet light to sterilize surfaces that might be contaminated with coronavirus. Those that must decontaminate large spaces, such as hospital rooms or aircraft cabins, use large, power-hungry mercury lamps to produce ultraviolet-C light. Companies around the world are working to improve the abilities of UV-C producing LEDs, to offer a more compact and efficient alternative. Earlier this month, Seoul Viosys showed what it says is the first 99.9 percent sterilization of SARS-COV-2, the coronavirus that causes COVID-19, using ultraviolet LEDs.
UV LEDs are deadly to viruses and bacteria, because the 100–280 nanometer wavelength C-band shreds genetic material. Unfortunately, it’s also strongly absorbed by nitrogen in the air, so sources have to be powerful to have an effect at a distance. (Air is such a strong barrier, that the sun’s UV-C doesn’t reach the Earth’s surface.) Working with researchers at Korea University, in Seoul, the company showed that its Violed LED modules could eliminate 99.9 percent of the SARS-COV-2 virus using a 30-second dose from a distance of three centimeters.
Unfortunately, the company did not disclose how many of its LEDs were used to achieve that. Assuming that it and the university researchers used a single Violed CMD-FSC-CO1A integrated LED module, a 30-second dose would have delivered at most 600 millijoules of energy. This is somewhat in-line with expectations. A study of UVC’s ability to kill influenza A viruses on N95 respirator masks indicated that about 1 joule per square centimeter would do the job.
As the electronic health record grows in detail, the possibilities for customized care are becoming a reality. This article features some useful links to things in the making.
While AI is driving value in all aspects of our lives, there are times where it’s hard to separate the aspirations of those who want to use it to do good from those leverag ing AI today to positively impact real change in health and medici ne.
I have the privilege of working with many talented leaders and organizations that are truly making health and medical services better by harnessing the power of healthcare’s data tsunami using AI and other analytical solutions.
COVID-19, p art t wo
While AI is driving value in all aspects of our lives, there are times where it’s hard to separate the aspirations of those who want to use it to do good from those leverag ing AI today to positively impact real change in health and medici ne.