Posted in robotics/AI
At 14, Anton received an old laptop that changed everything. Now he’s using AI to help himself and others achieve their potential.
Neither keyboards nor voice-to-text work well for Anton, a developer with cerebral palsy. He uses AI and LLMs to pursue his passion for programming and shows others how they can harness these technologies to accomplish more.
This talks about the changing dynamics of jobs and relation to AI. While there are a lot of apprehensions of AI killing jobs but it highlights that there are several new jobs being created by AI. It also stresses the need for professionals and students to reskill themselves in areas as diverse as AI and automation. So the argument that AI is going to kill jobs is not valid. Instead it enforces the argument that reskilling is most important.
LinkedIn: https://www.linkedin.com/in/tarah-ai-8316b7153/
Twitter: https://twitter.com/tarahtech.
#reskilling #AI #Automation #jobs #newskills #oldskills #reinvention #humanresources.
#AI #DeepLearning #ReinforcementLearning #MachineLearning #ML #DL #DataScience #ArtificialIntelligence #Classification #Jobs #Regression #Clustering #Intelligence #Learn #Intelligence #Knowledge #LearnFromHome #BI #BA #Analytics #Insights #Visualization #Graphs #Robots #Speech #BackPropagation #CNN #RNN #LSTM #NeuralNetworks #Network #Prediction #BigData #Hadoop
The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.
KEYWORDS: Artificial intelligence, clinical decision support, electronic health record systems.
Artificial intelligence (AI) and related technologies are increasingly prevalent in business and society, and are beginning to be applied to healthcare. These technologies have the potential to transform many aspects of patient care, as well as administrative processes within provider, payer and pharmaceutical organisations.
“It’s a time of huge uncertainty,” says Geoffrey Hinton from the living room of his home in London. “Nobody really knows what’s going to happen … I’m just sounding the alarm.”
In The Godfather in Conversation, the cognitive psychologist and computer scientist ‘known as the Godfather of AI’ explains why, after a lifetime spent developing a type of artificial intelligence known as deep learning, he is suddenly warning about existential threats to humanity.
A University of Toronto University Professor Emeritus, Hinton explains how neural nets work, the role he and others played in developing them and why the kind of digital intelligence that powers ChatGPT and Google’s PaLM may hold an unexpected advantage over our own. And he lays out his concerns about how the world could lose control of a technology that, paradoxically, also promises to unleash huge benefits – from treating diseases to combatting climate change.
Deep Learning (DL) performs classification tasks using a series of layers. To effectively execute these tasks, local decisions are performed progressively along the layers. But can we perform an all-encompassing decision by choosing the most influential path to the output rather than performing these decisions locally?
In an article published today in Scientific Reports, researchers from Bar-Ilan University in Israel answer this question with a resounding “yes.” Pre-existing deep architectures have been improved by updating the most influential paths to the output.
“One can think of it as two children who wish to climb a mountain with many twists and turns. One of them chooses the fastest local route at every intersection while the other uses binoculars to see the entire path ahead and picks the shortest and most significant route, just like Google Maps or Waze. The first child might get a head start, but the second will end up winning,” said Prof. Ido Kanter, of Bar-Ilan’s Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center, who led the research.
Join Dr. Ben Goertzel, the visionary CEO and Founder of SingularityNET, as he delves into the compelling realm of large language models. In this Dublin Tech Summit keynote presentation, Dr. Goertzel will navigate the uncharted territories of AI, discussing the imminent impact of large language models on innovation across industries. Discover the intricacies, challenges, and prospects of developing and deploying these transformative tools. Gain insights into the future of AI, as Dr. Goertzel unveils his visionary perspective on the role of large language models in shaping the AI landscape. Tune in to explore the boundless potentials of AI and machine learning in this thought-provoking session.
Themes: AI & Machine Learning | Innovation | Future of Technology | Language Models | Industry Transformation.
Keynote: Dr. Ben Goertzel, CEO and Founder, SingularityNET
#dubtechsummit
Robots based on soft materials are often better at replicating the appearance, movements and abilities of both humans and animals. While there are now countless soft robots, many of these are difficult to produce on a large-scale, due to the high cost of their components or their complex fabrication process.
Researchers at University of Coimbra in Portugal recently developed a new soft robotic hand that could be more affordable and easier to fabricate. Their design, introduced in Cyborg and Bionic Systems, integrates soft actuators with an exoskeleton, both of which can be produced using scalable techniques.
“Most robots are made of rigid materials,” Pedro Neto, one of the researchers who carried out the study, told Tech Xplore. “However, when we observe animals, we notice that their bodies can be composed of hard parts (skeletons) and soft parts (such as muscles). Some animals, like earthworms, are entirely soft-bodied. Taking inspiration from nature, we anticipate that the next generation of robots will incorporate components made of soft materials or, in some cases, they can be entirely soft-bodied.”
Large language models (LLMs) have become a general-purpose approach to embodied artificial intelligence problem-solving. When agents need to understand the semantic nuances of their environment for efficient control, LLMs’ reasoning skills are crucial in embodied AI. Recent methods, which they refer to as “programs of thought,” use programming languages as an improved prompting system for challenging reasoning tasks. Program-of-thought prompting separates the issues into executable code segments and deals with them one at a time, unlike chain-of-thought prompting. However, the relationship between the use of programming languages and the development of LLMs’ thinking skills has yet to receive enough research. When does program-of-thought suggesting work for reasoning2 remain the crucial question?
The complexity-impacted reasoning score (CIRS), a thorough metric for the link between code reasoning stages and their effects on LLMs’ reasoning abilities, is proposed in this paper. They contend that programming languages are inherently superior to serialized natural language because of their improved modeling of complex structures. Their innate procedure-oriented logic aids in solving difficulties involving several steps in thinking. Because of this, their suggested measure assesses the code complexity from both a structural and a logical standpoint. In particular, they compute the structural complexity of code reasoning stages (rationales) using an abstract syntax tree (AST). Their method uses three AST indicators (node count, node type, and depth) to keep all structural information in AST represented as a tree, which thoroughly comprehends code structures.
Researchers from Zhejiang University, Donghai Laboratory and National University of Singapore develop a way to determine logical complexity by combining coding difficulty with cyclomatic complexity, drawing inspiration from Halsted and McCabe’s idea. Thus, it is possible to consider the code’s operators, operands, and control flow. They can explicitly calculate the logic’s complexity within the code. They discover through an empirical investigation using their suggested CIRS that present LLMs have a restricted comprehension of symbolic information like code and that not all sophisticated code data can be taught and understood by LLMs. Low-complexity code blocks lack the necessary information, but high-complexity code blocks could be too challenging for LLMs to understand. To effectively improve the reasoning abilities of LLMs, only code data with an appropriate amount of complexity (structure & logic), both basic and detailed, are needed.
In a major breakthrough, scientists have built a tool to predict the odor profile of a molecule, just based on its structure. It can identify molecules that look different but smell the same, as well as molecules that look very similar but smell totally different. The research was published in Science.
Professor Jane Parker, University of Reading, said, “Vision research has wavelength, hearing research has frequency—both can be measured and assessed by instruments. But what about smell? We don’t currently have a way to measure or accurately predict the odor of a molecule, based on its molecular structure.”
“You can get so far with current knowledge of the molecular structure, but eventually you are faced with numerous exceptions where the odor and structure don’t match. This is what has stumped previous models of olfaction. The fantastic thing about this new ML generated model is that it correctly predicts the odor of those exceptions.”
