Robots can’t think or feel, despite what the researchers who build them want to believe.
Category: robotics/AI – Page 1,302
Human-like reasoning for an AI
In June 2022, Amazon re: MARS, the company’s in-person event that explores advancements and practical applications within machine learning, automation, robotics, and space (MARS), took place in Las Vegas. The event brought together thought leaders and technical experts building the future of artificial intelligence and machine learning, and included keynote talks, innovation spotlights, and a series of breakout-session talks.
Now, in our re: MARS revisited series, Amazon Science is taking a look back at some of the keynotes, and breakout session talks from the conference. We’ve asked presenters three questions about their talks, and provide the full video of their presentation.
On June 24, Alexa AI-Natural Understanding employees Craig Saunders, director of machine learning, and Devesh Pandey, principal product manager, presented their talk, “Human-like reasoning for an AI”. Their presentation focused on how Amazon is developing human-like reasoning for Alexa, including how Alexa can automatically recover from errors such as recognizing “turn on lights” in a noisy environment (instead of “turn off lights”) when the lights are already on.
Geoffrey Hinton’s Forward-Forward Algorithm Charts a New Path for Neural Networks
Turing Award winner and deep learning pioneer Geoffrey Hinton, one of the original proponents of backpropagation, has argued in recent years that backpropagation does not explain how the brain works. In his NeurIPS 2022 keynote speech, Hinton proposes a new approach to neural network learning: the Forward-Forward algorithm.
Creepy-crawly gel robots being trained to root out disease in body
Scientists have created a teeny tiny, creepy crawly-like robot they hope will travel through the human body to cure diseases. Made of gelatin, the 3D-printed device may eventually replace pills or intravenous injections that can cause problematic side effects. Bring on the killer robots! We need ’em more than ever.
Read more ❯.
ChatGPT: Optimizing Language Models for Dialogue
GPT Chat is a large language model trained by OpenAI, its function is to assist users in generating human-like text based on the input provided to it. It can assist with a wide range of tasks, such as answering questions, providing explanations, and generating original text. It’s designed to generate natural-sounding text, and it’s constantly learning and improving. It’s able to process and generate text at scale, making it a powerful tool for natural language processing and generation. It’s ultimate goal is to make it easier for people to interact with computers and access information using natural language.
Give it a try: https://openai.com/blog/chatgpt/
I had GPT Chat rewrite an article… More.
We’ve trained a model called which interacts in a conversational way. The dialogue format makes it possible for to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests. is a sibling model to InstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response.
We are excited to introduce to get users’ feedback and learn about its strengths and weaknesses. During the research preview, usage of is free. Try it now at chat.openai.com.
Try
Google Labs unveils Pitchfork, an AI that can convert old code to new code and rewrite itself
After six decades we have finally reached controlled fusion “ignition.” Here is how it works and what it means (and doesn’t mean):
At the Lawrence Livermore National Lab (LLNL) the National Ignition Facility (NIF) starts with the Injection Laser System (ILS), a ytterbium-doped optical fiber laser (Master Oscillator) that produces a single very lower power, 1,053 nanometer (Infrared Light) beam. This single beam is split into 48 Pre-Amplifiers Modules (PAMs) that create four beams each (192 total). Each PAM conducts a two-stage amplification process via xenon flash lamps.
Self-coding and self-updating AI algorithms appear to be on the horizon. There are talks about Pitchfork AI, a top-secret Google Labs project that can independently code, refactor, and use both its own and other people’s code.
This type of AI has actually been discussed for a long time, and DeepMind mentioned it at the beginning of the year along with the AlphaCode AI, which, according to them, “code programs in competitive level” as a middle developer. However, since February, there hasn’t been any more interesting news.
Researchers at DeepMind Created a 70B Parameter Language Model that Generates Statements Aligned with Humans with Diverse Viewpoints
Human preferences on any topic have become diverse. Coming up with a statement that the majority of the population agrees with seems to be a challenge. Researchers at DeepMind, an AI company, accepted this challenge, trained a large language model, and fine-tuned it. They have to assume that human preferences are static and homogeneous to build the model.
The model generates statements to maximize approval among a group of people with diverse preferences. The research team fine-tuned the 70 billion parameter model, which was provided by thousand moral and political questions, and human written responses were provided for those questions. Then a reward model was trained in order to give weight to different opinions. Their best model was able to achieve more than a 65 percent preference rate.
The model was very sensitive when they tested it by just feeding part of the responses of the group of people then, the rest of the people’s opinion, which was not included, had a significant variance. Thus, the individual contribution of each consensus is equally important. There are many complicated NLP tasks like reading comprehension, fluent language generation, etc., which helped form the foundations for this LLM.
