Cognixion received FDA breakthrough device designation for its brain computer Interface with augmented reality for assistive communication

A new AI system can translate a person’s brain activity — while listening to a story or silently imagining telling a story — into a continuous stream of text.

The system developed by researchers at The University of Texas at Austin might help people who are mentally conscious yet unable to physically speak, such as those debilitated by strokes, to communicate intelligibly again.

The work relies in part on a transformer model, similar to the ones that power Open AI’s ChatGPT and Google’s Bard.

Unlike other language decoding systems in development, this system does not require subjects to have surgical implants, making the process noninvasive.

Participants also do not need to use only words from a prescribed list.

Brain activity is measured using an fMRI scanner after extensive training of the decoder, in which the individual listens to hours of podcasts in the scanner.

Later, provided that the participant is open to having their thoughts decoded, their listening to a new story or imagining telling a story allows the machine to generate corresponding text from brain activity alone.

Mercedes designers, engineers, and manufacturers make use of VR technology in a number of ways during the process of conceptualizing and constructing vehicles.

This year, the company began a partnership with Nvidia focused on the use of the chipset manufacturer’s Omniverse platform. This is a VR-enabled digitaltwin collaborative environment for 3D design.

Meta partnered with BMW to create VR/XR

EEG + LLM interfaces have a high chance of being ferociously important soon.

This LLM guidance language from Microsoft is super interesting. Worth a read-through for sure.

Microsoft's artificial nose is able to recognize smells!

We are one step closer to human-like AI Robots.

With a simple gas sensor and a micro-controller, AI nose can identify the smell of bread, coffee, and many more scents.

The Artificial Nose uses a neural network to correlate the concentration of gases in the air to categories of smell.

When connected to an IoT platform, this nose could be used for a variety of scenarios: it could help to craft a real-time alert system for when foods have spoiled, or for detecting specific gases in the air.

Gas sensors measure the concentration of various gases emanating from a substance. An AI model extract the key characteristics of these gases and uses them to infer the corresponding smell.

The inferred result is shown to the user and can also be sent to Azure IoT to be further analyzed (ex. send alerts), or to contribute to updating and improving the model.

Building this Artificial Nose starts with the hardware, an off-the-self gas sensor and a microcontroller. The sensors can detect the concentration of gases—like Carbon monoxide (CO), Nitrogen dioxide (NO2), Ethyl alcohol(C2H5OH), and Volatile Organic Compounds (VOC)—found in the surrounding air.

The sensor is connected to a microcontroller, which is used to read and collect gas sensor data and feed it into the AI model.

The Artificial Nose “smells” by using a neural network to correlate the concentration of gases (CO, NO2, etc.) in the air—data inputs received from the gas sensor— to certain categories of smell (coffee, whiskey, bread).

The smell category is then displayed on the microcontroller screen along with a visual cue indicating the model’s degree of certainty in its correlation.

ChatGPT iOS app is live in the US, rolling out in other countries over upcoming weeks.

The Hong Kong Monetary Authority announced the commencement of the e-HKD Pilot Programme, including full-fledged payments, programmable payments, offline payments, tokenized deposits, settlement of Web3 transactions and settlement of tokenized assets.

18 companies selected Only Ripple Labs is a company in the field of cryptocurrency, and the others are Alipay Visa Mastercard and 9 banks. Ripple labs is involved in tokenized asset settlement projects.

Here are 10 new YC-backed AI companies you need to pay attention to:

1. Stack AI

Focus: Build LLM applications (i.e. ChatGPT) in minutes​.
Location: San Francisco, CA​​.
Website: stack-ai.com

2. Quazel

Focus: Language learning with a conversational AI Tutor​.
Location: Zürich, Switzerland​​.
Website: quazel.com

3. Magicflow

Focus: Webflow for AI development​.
Location: Tel Aviv-Yafo, Israel​.
Website: magicflow.ai

4. JustPaid io

Focus: AI-powered financial controller automating bill pay and invoicing​.
Location: Mountain View, CA​​.
Website: justpaid.io

5. Linum

Focus: Midjourney for Video​​.
Location: San Francisco, CA​.
Website: linum.ai

6. Layup

Focus: AI that builds workflows across company apps​​.
Location: San Francisco, CA​​.
Website: layupai.com

7. Buildt

Focus: AI devtool to search and understand large codebases​​.
Location: San Francisco, CA.
Website: buildt.ai

8. Type

Focus: The AI-first document editor​​.
Location: New York, NY​.
Website: type.ai

9. Persana AI

Focus: Intelligent sales copilot powered by fine-tuned models​.
Location: San Francisco, CA​​.
Website: persana.ai

10. Turntable

Focus: AI-native operating system for analytics teams​​.
Location: New York, NY​​.
Website: turntable.so

The ChatGPT effect rolls on. Snowflake in advanced talks to buy Neeva as database giant pushes into AI

Database software provider Snowflake has been in advanced talks to acquire Neeva, a search startup founded by former top Google ad tech executive Sridhar Ramaswamy.

Buying Neeva could help Snowflake offer AI software that helps companies search for information in internal documents and data, according to people who do business with Snowflake.

Neeva primarily sells an ad-free web-search app for consumers, but it developed software that combines search with large-language models, which are trained on text to understand the nuances of speech and writing.

That could fit with Snowflake’s efforts to help cloud customers use the kind of AI popularized by chatbots like ChatGPT that respond to conversational commands and can automate some business tasks.

Snowflake is trying to catch up to rivals such as Microsoft’s Azure and Google Cloud that already sell access to such AI software.

Jeff Bezos' space company has signed a contract with NASA to fly to the moon

NASA has awarded a NextSTEP-2 Appendix P Sustaining Lunar Development (SLD) contract to Blue Origin.

Blue Origin’s National Team partners include Lockheed Martin, Draper, Boeing, Astrobotic, and Honeybee Robotics.  

Under this contract, Blue Origin and its National Team partners will develop and fly both a lunar lander that can make a precision landing anywhere on the Moon’s surface and a cislunar transporter.

These vehicles are powered by LOX-LH2. The high-specific impulse of LOX-LH2 provides a dramatic advantage for high-energy deep space missions.

Nevertheless, lower performing but more easily storable propellants (such as hydrazine and nitrogen tetroxide as used on the Apollo lunar landers) have been favored for these missions because of the problematic boil-off of LOX-LH2 during their long mission timelines.

Game changes. A central question in neuroscience is how consciousness arises from the dynamic interplay of brain structure and function.

Here researchers decompose functional MRI signals from pathological and pharmacologically-induced perturbations of consciousness into distributed patterns of structure-function dependence across scales: the harmonic modes of the human structural connectome.

They show that structure-function coupling is a generalisable indicator of consciousness that is under bi-directional neuromodulatory control.

They find increased structure-function coupling across scales during loss of consciousness, whether due to anaesthesia or brain injury, capable of discriminating between behaviourally indistinguishable sub-categories of brain-injured patients, tracking the presence of covert consciousness.

The opposite harmonic signature characterises the altered state induced by LSD or ketamine, reflecting psychedelic-induced decoupling of brain function from structure and correlating with physiological and subjective scores.

Overall, connectome harmonic decomposition reveals how neuromodulation and the network architecture of the human connectome jointly shape consciousness and distributed functional activation across scales.

Top500 list of fastest supercomputer

https://top500.org/

As the deadlock in negotiations to raise the U.S. government's $31.4 trillion debt limit keeps markets tentative, Goldman Sachs warns a potential U.S. debt deal could lead to liquidity strain, causing a ripple effect on Bitcoin

Microsoft Researchers Introduce Reprompting: An Iterative Sampling Algorithm that Searches for the Chain-of-Thought (CoT) Recipes for a Given Task without Human Intervention.

Paper: arxiv.org/abs/2305.09993

Continuous wearable ultrasound using AI to auto-capture body deep tissue imaging and physiology during mobility for up to 12 hours.

Hong Kong's Securities Commission: crypto trading to be allowed for retail starting June 1, 2023.

Generative approaches to IR that store documents in a transformer model instead of search index, are on the rise.

Though not production ready yet, definitely a trend to watch, with incresingly good results on small collections. Here's some notable papers from this month.

1. How Does Generative Retrieval Scale to Millions of Passages?
Manages scaling to the full MS MARCO collection (8M passages), but results are not near SOTA yet.
arxiv.org/abs/2305.11841

2. Large Language Models are Built-in Autoregressive Search Engines.
Evaluates the capabilities of LLMs to memorize / dream up URL's that actually happen to exist.
arxiv.org/abs/2305.09612

3. TOME: A Two-stage Approach for Model-based Retrieval, by Ruiyang Ren et al.
Uses tokenized URLs as DOCids and also achieves scaling to the full MS MARCO collection with decent results.
arxiv.org/abs/2305.11161

4. Learning to Tokenize for Generative Retrieval.
This paper focuses on the learning of meaningful DOCids using a method called GenRet. New SOTA results of NQ320K, and additional evaluation on MS MARCO, and BEIR.
arxiv.org/abs/2304.04171

5. Recommender Systems with Generative Retrieval.
This paper uses the DSI framework for recommenders, achieves SOTA models on an Amazon dataset and improves retrieval of cold-start items.
arxiv.org/abs/2305.05065

Heading into the era of truly private, personalized AI assistants

Amazing to see LLMs like RedPajama-INCITE 3B run locally on mobile with hardware acceleration using WebAssembly and WebGPU. No need to write custom code for custom hardware.