With careful development and thoughtful approaches, Kazi believes the changes AI will bring to healthcare will be some of the most exciting technological advances of our lifetime.
Applying deep learning to large-scale genomic data of species or populations is providing new opportunities to understand the evolutionary forces that drive genetic diversity. This Review introduces common deep learning architectures and provides comprehensive guidelines to implement deep learning models for population genetic inference. The authors also discuss current opportunities and challenges for deep learning in population genetics.
A molecular assembler, as defined by K. Eric Drexler, is a “proposed device able to guide chemical reactions by positioning reactive molecules with atomic precision”. A molecular assembler is a kind of molecular machine. Some biological molecules such as ribosomes fit this definition. This is because they receive instructions from messenger RNA and then assemble specific sequences of amino acids to construct protein molecules. However, the term “molecular assembler” usually refers to theoretical human-made devices.
Beginning in 2007, the British Engineering and Physical Sciences Research Council has funded development of ribosome-like molecular assemblers. Clearly, molecular assemblers are possible in this limited sense. A technology roadmap project, led by the Battelle Memorial Institute and hosted by several U.S. National Laboratories has explored a range of atomically precise fabrication technologies, including both early-generation and longer-term prospects for programmable molecular assembly; the report was released in December, 2007. In 2008 the Engineering and Physical Sciences Research Council provided funding of 1.5 million pounds over six years for research working towards mechanized mechanosynthesis, in partnership with the Institute for Molecular Manufacturing, amongst others. Likewise, the term “molecular assembler” has been used in science fiction and popular culture to refer to a wide range of fantastic atom-manipulating nanomachines, many of which may be physically impossible in reality. Much of the controversy regarding “molecular assemblers” results from the confusion in the use of the name for both technical concepts and popular fantasies. In 1992, Drexler introduced the related but better-understood term “molecular manufacturing”, which he defined as the programmed “chemical synthesis of complex structures by mechanically positioning reactive molecules, not by manipulating individual atoms”.This article mostly discusses “molecular assemblers” in the popular sense. These include hypothetical machines that manipulate individual atoms and machines with organism-like self-replicating abilities, mobility, ability to consume food, and so forth. These are quite different from devices that merely (as defined above) “guide chemical reactions by positioning reactive molecules with atomic precision”. Because synthetic molecular assemblers have never been constructed and because of the confusion regarding the meaning of the term, there has been much controversy as to whether “molecular assemblers” are possible or simply science fiction. Confusion and controversy also stem from their classification as nanotechnology, which is an active area of laboratory research which has already been applied to the production of real products; however, there had been, until recently, no research efforts into the actual construction of “molecular assemblers”. Nonetheless, a 2013 paper by David Leigh’s group, published in the journal Science, details a new method of synthesizing a peptide in a sequence-specific manner by using an artificial molecular machine that is guided by a molecular strand. This functions in the same way as a ribosome building proteins by assembling amino acids according to a messenger RNA blueprint. The structure of the machine is based on a rotaxane, which is a molecular ring sliding along a molecular axle. The ring carries a thiolate group which removes amino acids in sequence from the axle, transferring them to a peptide assembly site. In 2018, the same group published a more advanced version of this concept in which the molecular ring shuttles along a polymeric track to assemble an oligopeptide that can fold into a α-helix that can perform the enantioselective epoxidation of a chalcone derivative (in a way reminiscent to the ribosome assembling an enzyme). In another paper published in Science in March 2015, chemists at the University of Illinois report a platform that automates the synthesis of 14 classes of small molecules, with thousands of compatible building blocks. In 2017 David Leigh’s group reported a molecular robot that could be programmed to construct any one of four different stereoisomers of a molecular product by using a nanomechanical robotic arm to move a molecular substrate between different reactive sites of an artificial molecular machine. An accompanying News and Views article, titled ‘A molecular assembler’, outlined the operation of the molecular robot as effectively a prototypical molecular assembler.
The qb SoftHand2 Research is the stronger, smarter and more versatile evolution of qb SoftHand Research: an anthropomorphic robotic hand with 19 disclosable self-healing finger joints. It is always adaptable and robust, easy-to-use and flexible.
The qb SoftHand2 Research represents a compromise between complexity and dexterity. This new hand is capable of performing both precision and power grips, as well as manipulating objects while maintaining a stable grip.
The introduction of second synergy hallows the qb SoftHand2 Research to manipulate objects intended for interaction with the human hand, without changing the wrist orientation.
The Indian Space Research Organisation (ISRO) is set to launch a humanoid robot into space as part of its Gaganyaan mission, its first human spaceflight mission.
According to the ISRO, the Gaganyaan project was established to demonstrate human spaceflight capability by launching a three-person crew to an orbit of 248 miles for a three-day mission and then bring them back to Earth safely, landing in Indian sea waters.
A robot moves a toy package of butter around a table in the Intelligent Robotics and Vision Lab at The University of Texas at Dallas. With every push, the robot is learning to recognize the object through a new system developed by a team of UT Dallas computer scientists.
The new system allows the robot to push objects multiple times until a sequence of images are collected, which in turn enables the system to segment all the objects in the sequence until the robot recognizes the objects. Previous approaches have relied on a single push or grasp by the robot to “learn” the object.
The team presented its research paper at the Robotics: Science and Systems conference held July 10–14 in Daegu, South Korea. Papers for the conference were selected for their novelty, technical quality, significance, potential impact and clarity.
If modern artificial intelligence has a founding document, a sacred text, it is Google’s 2017 research paper “Attention Is All You Need.” This paper introduced a new deep learning architecture known as the transformer, which has gone on to revolutionize the field of AI over the past half-decade.
The generative AI mania currently taking the world by storm can be traced directly to the invention of the transformer. Every major AI model and product in the headlines today—ChatGPT, GPT-4, Midjourney, Stable Diffusion, GitHub Copilot, and so on—is built using transformers.
Transformers are remarkably general-purpose: while they were initially developed for language translation specifically, they are now advancing the state of the art in domains ranging from computer vision to robotics to computational biology.
In short, transformers represent the undisputed gold standard for AI technology today.
Racially biased artificial intelligence (AI) is not only misleading, it can be right down detrimental, destroying people’s lives. This is a warning University of Alberta Faculty of Law assistant professor Dr. Gideon Christian issued in a press release by the institution.
Christian is most notably the recipient of a $50,000 Office of the Privacy Commissioner Contributions Program grant for a research project called Mitigating Race, Gender and Privacy Impacts of AI Facial Recognition Technology. The initiative seeks to study race issues in AI-based facial recognition technology in Canada. Christian is considered an expert on AI and the law.
“There is this false notion that technology unlike humans is not biased. That’s not accurate,” said Christian, PhD.
Artificial intelligence (AI) has been helping humans in IT security operations since the 2010s, analyzing massive amounts of data quickly to detect the signals of malicious behavior. With enterprise cloud environments producing terabytes of data to be analyzed, threat detection at the cloud scale depends on AI. But can that AI be trusted? Or will hidden bias lead to missed threats and data breaches?
Bias can create risks in AI systems used for cloud security. There are steps humans can take to mitigate this hidden threat, but first, it’s helpful to understand what types of bias exist and where they come from.