Meta AI Decoding Brain Speech with 73% Accuracy 🤯💥
Plus: Wayve Enhancing Autonomous Vehicle Training, OpenAI Considers In-house AI Chips to Reduce Nvidia Dependency.
Hello Engineering Leaders and AI Enthusiasts!
Welcome to the 120th edition of The AI Edge newsletter. This edition brings you Meta's Innovative AI Decodes Speech from Brain Signals with 73% Accuracy.
And a huge shoutout to our incredible readers. You all rock! 😊
In today’s edition:
🤯 Meta AI Decoding Brain Speech with 73% Accuracy
👏🚗Wayve's GAIA-1 9B Enhancing Autonomous Vehicle Training
🔌 OpenAI Considers In-house AI Chips to Reduce Nvidia Dependency
🧠 Knowledge Nugget: Everything That Can Be LLM-ed, Will Be by
Let’s go!
Meta AI Decoding Brain Speech ~ 73% Accuracy
Meta Researchers have developed a model that can decode speech from non-invasive brain recordings with a high level of accuracy. The model was trained using contrastive learning and could identify speech segments from magneto-encephalography signals. The model's performance allows for the decoding of words and phrases that were not included in the training set.
The study highlights the importance of contrastive objectives, pretrained representations of speech, and a common convolutional architecture for achieving accurate speech decoding from brain activity. This research offers a promising approach to decoding language from brain recordings without the need for invasive procedures.
Why does this matter?
Meta’s research could one day help restore communication for stroke patients, ALS, etc. The accuracy is 73% which is not high enough for natural conversations. But this is a hugely promising step toward brain-computer interfaces. It will surely make a difference in the intersection of neuroscience and AI by eventually helping patients with neurological conditions communicate just by thinking.
Wayve's GAIA-1 9B Enhancing Autonomous Vehicle Training
British startup Wayve announces the release of GAIA1, A 9B parameter world model trained on 4,700 hours of driving data. This model is for autonomous driving that uses text, image, video, and action data to create synthetic videos of various traffic situations for training purposes.
It is 480 times larger than the previous version and offers incredible results. And designed to understand and decode key driving concepts, providing fine-grained control of vehicle behavior and scene characteristics to improve autonomous driving systems.
Why does this matter?
GAIA-1 is not just about generating videos; it is a complete world model that can simulate the future, making it crucial for informed decision-making in autonomous driving. This development is game-changing for autonomous driving as it enhances safety, provides synthetic training data, and improves long-tail robustness by combining edge cases.
OpenAI Considers In-house AI Chips to Reduce Nvidia Dependency
OpenAI is considering developing its own AI chips and has even evaluated a potential acquisition target, sources say. While no final decision has been made, recent discussions within the company have centered on addressing the scarcity of high-cost AI chips integral to OpenAI's operations.
Why does this matter?
Developing its own chips could give OpenAI more control over its hardware and potentially reduce costs. This move aligns with OpenAI's goal of becoming a more self-sufficient organization and reducing its dependence on external chip suppliers like Nvidia.
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Knowledge Nugget: Everything That Can Be LLM-ed, Will Be
This interesting article by
discusses the rise of Enterprise LLMs and their potential impact on organizations. LLMs, such as McKinsey's "Lilli" and Morningstar's "Mo," are being used to extract knowledge and insights from employees and create AI systems that embody a company's ethos.
These LLMs can be used for various purposes, including marketing, sales, product interface, and customer care. Large consultancies like McKinsey and PwC are leading the way in developing sophisticated Enterprise LLM-based experiences, which allow organizations to engage stakeholders in natural, conversational ways at scale.
Why does this matter?
This article sheds light on the growing significance of Enterprise Large Language Models in the corporate realm. It will help understand how the rise of Enterprise LLMs is crucial as it promises to improve knowledge management, enhance customer experiences, and confer competitive advantages, making it a transformative force in the business world.
What Else Is Happening❗
✅ IBM launches AI-powered Threat Detection and Response Services
The services ingest and analyze security data from various technologies and vendors, reducing noise and escalating critical threats. The AI models continuously learn from real-world client data, automatically closing low-priority & false positive alerts while escalating high-risks. (Link)
✅ You can now use Mistral 7B on Poe
Poe made it available through its API launch. Users can now access Mistral 7B on multiple devices and operating systems. (Link)
✅ Likewise partners with OpenAI to deliver entertainment recommendations
They launched Pix AI chatbot, accessed through text message, email, app, website, or by speaking to a TV app using a voice remote. It learns the preferences of individuals & provides tailored recommendations. Likewise has a user base of over 6 million. (Link)
✅ Artifact, the news app offering users the ability to create AI-gen images to accompany their posts
Users can enter a prompt specifying the subject, medium, and style, and the AI will generate images taking only seconds. If users are unsatisfied with the results, they can generate another image or revise the prompt. (Link)
✅ Microsoft introduces AI meddling to files with Copilot
It will include a new web interface called OneDrive Home, providing a portal for users to access their files And AI-generated file suggestions under the "For You" section. Other upcoming features include opening desktop apps from the browser, integration with Teams and Outlook, and offline functionality for working on files without internet access. The updates are set to roll out in December. (Link)
That's all for now!
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