For thousands of years, mathematicians have adapted to the latest advances in logic and reasoning. Are they ready for artificial intelligence?
An android robot, EveR 6, took the conductor’s podium in Seoul on Friday evening to lead a performance by South Korea’s national orchestra, marking the first such attempt in the country.
The two-armed robot, designed by the Korea Institute of Industrial Technology, made its debut at the National Theater of Korea, leading musicians in the country’s national orchestra.
The robot, with a humanoid face, first bowed to the audience and started waving its arms to control the tempo of the live show.
The concept of a computational consciousness and the potential impact it may have on humanity is a topic of ongoing debate and speculation. While Artificial Intelligence (AI) has made significant advancements in recent years, we have not yet achieved a true computational consciousness that can replicate the complexities of the human mind.
It is true that AI technologies are becoming more sophisticated and capable of performing tasks that were previously exclusive to human intelligence. However, there are fundamental differences between Artificial Intelligence and human consciousness. Human consciousness is not solely based on computation; it encompasses emotions, subjective experiences, self-awareness, and other aspects that are not yet fully understood or replicated in machines.
The arrival of advanced AI systems could certainly have transformative effects on society and our understanding of humanity. It may reshape various aspects of our lives, from how we work and communicate to how we approach healthcare and scientific discoveries. AI can enhance our capabilities and provide valuable tools for solving complex problems.
However, it is important to consider the ethical implications and potential risks associated with the development of AI. Ensuring that AI systems are developed and deployed responsibly, with a focus on fairness, transparency, and accountability, is crucial.
Recent progress in AI has been startling. Barely a week’s gone by without a new algorithm, application, or implication making headlines. But OpenAI, the source of much of the hype, only recently completed their flagship algorithm, GPT-4, and according to OpenAI CEO Sam Altman, its successor, GPT-5, hasn’t begun training yet.
It’s possible the tempo will slow down in coming months, but don’t bet on it. A new AI model as capable as GPT-4, or more so, may drop sooner than later.
This week, in an interview with Will Knight, Google DeepMind CEO Demis Hassabis said their next big model, Gemini, is currently in development, “a process that will take a number of months.” Hassabis said Gemini will be a mashup drawing on AI’s greatest hits, most notably DeepMind’s AlphaGo, which employed reinforcement learning to topple a champion at Go in 2016, years before experts expected the feat.
Research into AI is experiencing a boom, so we have rounded up the best of news from the past month to help you keep up to date.
AI applications are summarizing articles, writing stories and engaging in long conversations — and large language models are doing the heavy lifting.
A large language model, or LLM, is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other forms of content based on knowledge gained from massive datasets.
Large language models are among the most successful applications of transformer models. They aren’t just for teaching AIs human languages, but for understanding proteins, writing software code, and much, much more.
Michelle Taite global CMO at Mailchimp, was interviewed about AI and marketing at the Cannes Lions International Festival of Creativity.
VIDEO : Masahiko Inami and his team at the University of Tokyo have created a wearable — and exchangeable — multi-armed device to explore the social interaction between multiple users of the robotic limbs. The bot has six sockets that can hold fingers, arms or a claw.
Steam, one of the world’s largest video game storefronts is reportedly not having any of this AI generated stuff.
The data centers and high-performance computers that run artificial intelligence programs, such as large language models, aren’t limited by the sheer computational power of their individual nodes. It’s another problem—the amount of data they can transfer among the nodes—that underlies the “bandwidth bottleneck” that currently limits the performance and scaling of these systems.
The nodes in these systems can be separated by more than one kilometer. Since metal wires dissipate electrical signals as heat when transferring data at high speeds, these systems transfer data via fiber-optic cables. Unfortunately, a lot of energy is wasted in the process of converting electrical data into optical data (and back again) as signals are sent from one node to another.
In a study published in Nature Photonics, researchers at Columbia Engineering demonstrate an energy-efficient method for transferring larger quantities of data over the fiber-optic cables that connect the nodes. This new technology improves on previous attempts to transmit multiple signals simultaneously over the same fiber-optic cables. Instead of using a different laser to generate each wavelength of light, the new chips require only a single laser to generate hundreds of distinct wavelengths of light that can simultaneously transfer independent streams of data.
