Wonder drugs, environmental sustainability or Skynet apocalypse: Hundreds of experts weigh in on what life might be for A.I.-fueled 2035 in new Pew Research report.
Category: robotics/AI – Page 887
The 17 goals were set by the UN in 2015 and over the years, these goals have become unachievable.
The United Nations’ ‘AI for Good’ Summit is underway in Geneva and will showcase specialized robots to help the organization reach its 17 Social Development Goals (SDGs).
The goals were set in 2015, and over the years, these goals have become improbable, owing to the increasing costs of meeting the targets. The United Nations has been fighting issues like hunger, poverty, and climate change, whose prices have risen 25 PERCENT to $176 trillion from 2021 to 2022, reported Reuters.
New research from the University of Montana and its partners suggests artificial intelligence can match the top 1% of human thinkers on a standard test for creativity.
The study was directed by Dr. Erik Guzik, an assistant clinical professor in UM’s College of Business. He and his partners used the Torrance Tests of Creative Thinking, a well-known tool used for decades to assess human creativity.
The researchers submitted eight responses generated by ChatGPT, the application powered by the GPT-4 artificial intelligence engine. They also submitted answers from a control group of 24 UM students taking Guzik’s entrepreneurship and personal finance classes. These scores were compared with 2,700 college students nationally who took the TTCT in 2016. All submissions were scored by Scholastic Testing Service, which didn’t know AI was involved.
One way to achieve this is to combine GPTs with causal AI—a precise and trustworthy type of AI that provides rich and accurate context, which is particularly valuable in cloud observability, analytics and automation.
Causal AI observes the actual relationships within a system, such as a multicloud technology stack, and delivers detailed and precise answers in near real time based on these observations. These answers enable users to discern the cause, type, severity, risk, impact and location of any issue flagged by the AI with very high precision based on real-time observed facts and their interdependencies.
In the future, DevOps teams can use automated prompt engineering to feed real-time data and causal AI-derived context to their GPT. As a result, the answers they receive will be more relevant, accurate and actionable.
After being one of the first plugins to ever come to ChatGPT, Wolfram has now gone all in on the LLM wave. In the latest version 13.3 update, the Wolfram language has added support for LLM technology, as well as integrating an AI model into the Wolfram Cloud.
This update comes on the heels of Wolfram slowly building the tooling for making the language LLM-ready. The update puts LLMs directly into the language with the introduction of an LLM subsystem for the language. It also builds on the LLM functions technology added in May, which ‘packages’ AI powers into a callable function, with the new subsystem now being user-addressable.
With these new updates, developers have a whole new way of interfacing with their data. This approach combines Stephen Wolfram’s idea of natural language programming along with the Wolfram language’s symbolic programming, creating a force to be reckoned with. What’s more, with the Wolfram language API, this can be plugged in to larger systems, delivering amazing power through a natural language interface.
After finding early success with a robot crop-spraying plane, the California startup Pyka has developed an autonomous cargo carrier that could be among the first of a wave of novel electric aircraft to come to market that could change how goods get around.
AI has applications for archeology too! That’s actually a career I considered before computers. I wish I’d gone with that. Long story but computers were kind of a disaster for me.
Nearly a million texts of Akkadian were untranslated before an A.I. was developed that can do it in seconds.
EPFL scientists show that even a few simple examples are enough for a quantum machine-learning model, the “quantum neural networks,” to learn and predict the behavior of quantum systems, bringing us closer to a new era of quantum computing.
Imagine a world where computers can unravel the mysteries of quantum mechanics, enabling us to study the behavior of complex materials or simulate the intricate dynamics of molecules with unprecedented accuracy.
Thanks to a pioneering study led by Professor Zoe Holmes and her team at EPFL, we are now closer to that becoming a reality. Working with researchers at Caltech, the Free University of Berlin, and the Los Alamos National Laboratory, they have found a new way to teach a quantum computer how to understand and predict the behavior of quantum systems. The research has been published in Nature Communications.
As Nvidia’s recent surge in market capitalization clearly demonstrates, the AI industry is in desperate need of new hardware to train large language models (LLMs) and other AI-based algorithms. While server and HPC GPUs may be worthless for gaming, they serve as the foundation for data centers and supercomputers that perform highly parallelized computations necessary for these systems.
When it comes to AI training, Nvidia’s GPUs have been the most desirable to date. In recent weeks, the company briefly achieved an unprecedented $1 trillion market capitalization due to this very reason. However, MosaicML now emphasizes that Nvidia is just one choice in a multifaceted hardware market, suggesting companies investing in AI should not blindly spend a fortune on Team Green’s highly sought-after chips.
The AI startup tested AMD MI250 and Nvidia A100 cards, both of which are one generation behind each company’s current flagship HPC GPUs. They used their own software tools, along with the Meta-backed open-source software PyTorch and AMD’s proprietary software, for testing.
Generative AI’s potential to unleash creativity, accelerate discovery, and enhance efficiency could add trillions to Asian economies.
When it comes to the ability to generate, arrange, and analyze content, generative AI is a gamechanger—one with transformative social and economic potential.
As a technology that is democratized—one that doesn’t simply exist in a faraway lab or tech community in Silicon Valley, for instance—generative AI lowers the barriers to participation. In the age of generative AI, anyone can be a creator. But this also entails a profound workforce shift, changing the processes of production within the economy and, in turn, the types of tasks that are undertaken and the… More.