OpenAI warns DeepSeek 'distilled' its AI models, reports
OpenAI has accused Chinese AI start-up DeepSeek of using its proprietary models to train an open-source competitor.
www.newsweek.com
Well worth a read.
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
www.newsweek.com
Well worth a read.
Tothemoon24
Top 20
Welcome to the Keyword Spotting Revolution: Where Intelligent Voice Interaction meets Real-World
Applications
Smart devices, wearables and even consumer appliances are becoming increasingly dependant on voice interaction, yet technology solutions are challenged with efficiently controlling the energy required to support these very specific interactions within the devices.
How to Determine when Device
interaction is Needed
Identify Keywords to match
Are they recognized?
Device interacts and listens.
If the keywords are not recognized, the device does not attempt to process any data, which elongates battery life and provides an additional layer of user privacy.This is Keyword Spotting (KWS)
Devices detect specific wake words and phrases and interact only when there is a match.
- Reduces User Latency
- Ensures Privacy
- Improves Power Efficiency
- Significantly Reduces
You no longer need to be a large cloud provider to offer a “hello” and wake up service for devices.
Why Keyword Spotting?
Keyword Spotting is a technology that listens for specific words or phrases in an audio stream, triggering a response from a device. Once known only for waking up a device service that routes the rest of the voice from your home or smartphone device to a server in the cloud, Keyword Spotting can be deployed locally to interpret a wide set of in context commands, such as “lock the door” or “set the microwave for 1 minute.”
The key is the device’s ability to locally monitor audio input in a low-power state until it detects the specified keyword, allowing the system to save battery or compute power until needed, which is particularly important for devices such as wearables and other IoT devices. Having a small library of key words enables local control of a device without any other interface.
Key Benefits
Lower Cost of Ownership
Local edge processing allows devices to operate without relying on cloud connectivity, reducing operational costs.
Reduced Latency
Responses are nearly instantaneous, providing a natural voice interface, without the need for round trips to the cloud and back.
Enhanced Privacy
Audio data stays on the device, there’s no risk of personal information being eavesdropped on by remote servers.
There are a host of examples of how this can benefit users in scenarios like a vehicle,where commands like “turn on the lights” or “check for maintenance” offer a betterexperience than searching for switches or menus. It enables instant, connection-freeresponses, which is crucial for safety.
Just a thought - contemplating the Dec 10 announcement relating to the defense contract that was won.
Is there any chance that the negotiations with the sub-contractor to enter the agreement have already been made/finalised and that reaching the agreement was not considered to be price sensitive news?
It states in the announcement that there are no additional considerations to make the contract legally binding. Is there some chance that a privacy conscious company has finalised the agreement and asked BRN not to announce?
Given BRN have already stated the financial terms of the contract they may consider their obligation to the market fulfilled?
Is there any chance that the negotiations with the sub-contractor to enter the agreement have already been made/finalised and that reaching the agreement was not considered to be price sensitive news?
It states in the announcement that there are no additional considerations to make the contract legally binding. Is there some chance that a privacy conscious company has finalised the agreement and asked BRN not to announce?
Given BRN have already stated the financial terms of the contract they may consider their obligation to the market fulfilled?
Thanks for putting up the details TothemoonView attachment 76935
Welcome to the Keyword Spotting Revolution: Where Intelligent Voice Interaction meets Real-World
Applications
Smart devices, wearables and even consumer appliances are becoming increasingly dependant on voice interaction, yet technology solutions are challenged with efficiently controlling the energy required to support these very specific interactions within the devices.
How to Determine when Device
interaction is Needed
Identify Keywords to match
Are they recognized?
Device interacts and listens.
If the keywords are not recognized, the device does not attempt to process any data, which elongates battery life and provides an additional layer of user privacy.
This is Keyword Spotting (KWS)
Devices detect specific wake words and phrases and interact only when there is a match.
This technology powers the functionality behind familiar voice-activated systems, making them responsive and user-friendly. However, as devices evolve and user expectations grow, Keyword Spotting is now occurring locally on devices, without cloud connectivity.
- Reduces User Latency
- Ensures Privacy
- Improves Power Efficiency
- Significantly Reduces
You no longer need to be a large cloud provider to offer a “hello” and wake up service for devices.
Why Keyword Spotting?
Keyword Spotting is a technology that listens for specific words or phrases in an audio stream, triggering a response from a device. Once known only for waking up a device service that routes the rest of the voice from your home or smartphone device to a server in the cloud, Keyword Spotting can be deployed locally to interpret a wide set of in context commands, such as “lock the door” or “set the microwave for 1 minute.”
The key is the device’s ability to locally monitor audio input in a low-power state until it detects the specified keyword, allowing the system to save battery or compute power until needed, which is particularly important for devices such as wearables and other IoT devices. Having a small library of key words enables local control of a device without any other interface.
Key Benefits
Lower Cost of Ownership
Local edge processing allows devices to operate without relying on cloud connectivity, reducing operational costs.
Reduced Latency
Responses are nearly instantaneous, providing a natural voice interface, without the need for round trips to the cloud and back.
Enhanced Privacy
Audio data stays on the device, there’s no risk of personal information being eavesdropped on by remote servers.
There are a host of examples of how this can benefit users in scenarios like a vehicle,where commands like “turn on the lights” or “check for maintenance” offer a betterexperience than searching for switches or menus. It enables instant, connection-freeresponses, which is crucial for safety.
I'm not very handy with tech
Pom down under
Top 20
Is today going to be a pump day from the traders and then a sell off day tomorrow?
Braintonic
Regular
I'll go sell off today and a pump late tomorrow before the weekend. FOMO
Based on "absolutely nothing!"
Very hard to see with this short sightedness but the long view is very clear to me.
What's your game plan Pom? Sell a bit tomorrow, then buy again next week?
Pom down under
Top 20
Labsy
Regular
My 2cent thought for the month
Deepseek, a new wiz bang LLM mysteriously condensed and tweeked to function in current edge devices like laptops and mobiles... Meta and Google feverishly dissecting and picking at the open source to see how they can improve their own LLM's on same principles. Privacy is a big advantage of apple who are wanting to implement LLM on device The market rewards Meta and Apple with increase in sp movement, in news of emergence of Deepseek open source
This is a massive plus for us. I know we are actively working on and promoting the potential of using akida to run Small language models on edge. This will be a match made in heaven.
Have I got this totally wrong guys?
Deepseek, a new wiz bang LLM mysteriously condensed and tweeked to function in current edge devices like laptops and mobiles... Meta and Google feverishly dissecting and picking at the open source to see how they can improve their own LLM's on same principles. Privacy is a big advantage of apple who are wanting to implement LLM on device The market rewards Meta and Apple with increase in sp movement, in news of emergence of Deepseek open source
This is a massive plus for us. I know we are actively working on and promoting the potential of using akida to run Small language models on edge. This will be a match made in heaven.
Have I got this totally wrong guys?
uiux
Regular
Humble Genius
Regular
Why is BRN going into trading status "pre open" sporadically this morning?
This is on the Westpac app.....
This is on the Westpac app.....
supersonic001
Regular
#voiceinteraction #keywordspotting #aiinnovation #smartdevices | BrainChip
Welcome to the Keyword Spotting Revolution As smart devices, wearables, and consumer appliances increasingly rely on voice interaction, they face a critical challenge: efficiently managing energy consumption to support these interactions. Keyword spotting technology offers a game-changing...
Touchstone
Emerged
Westpac app is having problems.
supersonic001
Regular
What's New This Month: January 2025 Edition | Edge Impulse (a Qualcomm company)
🚀 Our First Newsletter of 2025 is Here! 🚀 We’re kicking off the year with some exciting updates: 🔍 Edge AI Innovations – Explore breakthroughs transforming the AI landscape. 📊 Market Insights – Key trends and data to help you navigate the year ahead. 🎥 On-Demand Webinar – Missed our live...
Taproot
Regular
BrainChip Powers Cyberthreat Tool
January 23, 2025by The Neuron
The integration of BrainChip’s ultra-low power neuromorphic AI into Quantum Ventura’s CyberNeuro-RT technology is poised to revolutionize cybersecurity protection for network access devices. By leveraging the Akida processor, this innovative solution enables on-chip learning and threat detection at the edge, providing a robust defense against cyber threats without relying on cloud-based servers or central processing units.
This collaboration has yielded a cutting-edge technology that can identify and adapt to emerging threats in real-time, utilizing BrainChip’s event-based neuromorphic principles to mimic the human brain and analyze essential sensor inputs with unparalleled efficiency and precision. As the demand for robust cybersecurity solutions continues to grow, this partnership is set to play a pivotal role in safeguarding WiFi access, home routers, small enterprise routers, and other network access devices from malicious attacks, ultimately enhancing the security and integrity of our increasingly interconnected world.
Introduction to Neuromorphic Processing for Cybersecurity
The integration of neuromorphic processing into cybersecurity solutions has the potential to revolutionize the way threats are detected and mitigated. BrainChip Holdings Ltd, a company specializing in ultra-low power, fully digital, event-based, neuromorphic AI, has announced its collaboration with Quantum Ventura to provide cybersecurity protection for WiFi access, home routers, small enterprise routers, and other network access devices. This partnership aims to leverage the Akida processor, BrainChip’s flagship product, to facilitate on-chip learning for deployment of network-specific attack detection.The increasing need for cybersecurity solutions that can thwart attacks through otherwise unsecure devices connected to the network has driven the development of innovative technologies like Quantum Ventura’s CyberNeuro-RT (CNRT). This technology offering, developed in partnership with Lockheed Martin Co.’s MFC Division and Pennsylvania State University, utilizes BrainChip’s neuromorphic processing capabilities to provide managed cybersecurity support for edge devices that cannot rely on a central server to identify threats and attacks due to cost or power issues. The integration of Akida into CNRT enables the detection of threats across multiple devices that would otherwise be vulnerable to exploitation.
The Akida neural processor and AI IP can identify unknown repeating patterns in vast amounts of noisy data, making it an asset in cyberthreat detection. By learning what normal network traffic patterns look like, Akida can detect malware, attack signatures, and other types of malicious activity. Its unique ability to learn on-device in a secure fashion, without the need for cloud retraining, allows it to quickly adapt to emerging threats.
Neuromorphic Processing and Cyberthreat Detection
Neuromorphic processing, inspired by the human brain’s neural structure and function, offers a promising approach to cyberthreat detection. The Akida processor, with its event-based technology, is inherently lower power than conventional neural network accelerators, providing energy efficiency with high performance for partners to deliver AI solutions previously not possible on even battery-operated or fan-less embedded edge devices. BrainChip’s IP supports incremental learning, on-chip learning, and high-speed inference with unsurpassed performance in micro-watt to milli-watt power budgets, making it ideal for advanced AI/ML devices such as intelligent sensors, medical devices, and autonomous systems.The integration of neuromorphic processing into cybersecurity solutions can provide high-quality protection against cyber threats. Akida’s on-chip learning can adapt to new threats and redirect unknown threats to the cloud, providing faster and more cost-efficient analysis capabilities than otherwise possible. This approach enables effective edge compute to be universally deployable across real-world applications, reducing latency while improving privacy and data security.
Edge AI and Cybersecurity
Edge AI, which involves processing data at the point of acquisition, is becoming increasingly important in cybersecurity. The ability to analyze data locally, without relying on cloud connectivity, can significantly improve response times and reduce the risk of data breaches. BrainChip’s Akida processor, with its neuromorphic architecture, is well-suited for edge AI applications, enabling Edge learning locally to the chip, independent of the cloud.The benefits of edge AI in cybersecurity are numerous. By processing data at the edge, latency is reduced, and real-time threat detection becomes possible. Additionally, edge AI can improve privacy and data security by minimizing the amount of sensitive data that needs to be transmitted to the cloud for analysis. As the number of connected devices continues to grow, the importance of edge AI in cybersecurity will only continue to increase.
Quantum Ventura’s CyberNeuro-RT Solution
Quantum Ventura’s CyberNeuro-RT solution is a systems integration and environmental technology innovation company that aims to deliver customer-centric advanced solutions for U.S. Federal & State Governments and Private Sector customers. The CNRT solution, developed in partnership with BrainChip, Lockheed Martin Co.’s MFC Division, and Pennsylvania State University, utilizes neuromorphic processing to provide managed cybersecurity support for edge devices.The CNRT solution has the potential to revolutionize the way cyber threats are detected and mitigated. By leveraging BrainChip’s Akida processor, Quantum Ventura can provide high-quality protection against cyber threats, reducing the risk of data breaches and improving overall cybersecurity posture. As the demand for effective cybersecurity solutions continues to grow, Quantum Ventura’s CNRT solution is well-positioned to meet the needs of government and private sector customers.
Conclusion
The integration of neuromorphic processing into cybersecurity solutions has the potential to significantly improve the way threats are detected and mitigated. BrainChip’s collaboration with Quantum Ventura to provide cybersecurity protection for WiFi access, home routers, small enterprise routers, and other network access devices is a promising development in this field. As the importance of edge AI in cybersecurity continues to grow, companies like BrainChip and Quantum Ventura are well-positioned to deliver innovative solutions that meet the needs of government and private sector customers.
BrainChip Powers Cyberthreat Tool
The integration of BrainChip's ultra-low power neuromorphic AI into Quantum Ventura's CyberNeuro-RT technology is poised to revolutionize cybersecurity protection for network access devices. By leveraging the Akida processor, this innovative solution enables on-chip learning and threat detection...
Taproot
Regular
CyberNeuro-RT
www.quantumventura.com
An IoT, AI/ML-driven, highly-scalable, real-time network defense & threat intelligence tool with CPU, GPU or low-power neuromorphic chip deployment
At-the-edge Neuromorphic Processing
- Two offerings from the leading neuromorphic developers: Intel and Brainchip
- Small form factor, magnitudes less power consumption than GPU
- On-chip learning for deployment network specific attack detection
Intel Loihi
Brainchip Akida
TheUnfairAdvantage
Regular
Interesting that the image of Intel Loihi appears in a USB form factor. Wasn’t aware Intel reduced Loihi down to this size. Last I checked it fit in a brief caseCyberNeuro-RT
An IoT, AI/ML-driven, highly-scalable, real-time network defense & threat intelligence tool with CPU, GPU or low-power neuromorphic chip deployment
At-the-edge Neuromorphic Processing
- Two offerings from the leading neuromorphic developers: Intel and Brainchip
- Small form factor, magnitudes less power consumption than GPU
- On-chip learning for deployment network specific attack detection
Intel Loihi
Brainchip Akida
Didn’t Brainchip delay producing a USB version of Akida. Didn’t Brainchip also not tape out Akida 2.0.
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