BRN Discussion Ongoing
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Wags
Regular
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AusEire
Founding Member. It's ok to say No to Dot Joining
"You can't be relaxed"
Well actually you can be relaxed because if you have done your DD on the company then these short-term price movements won't bother you so much.
The only people (IMO) that are worried about short term price movements are those who are either looking to make a quick buck or are trading. (Obviously there will be a few long-term shareholders that will be angry or uncomfortable but that's life).
Everyone has their own strategy. Telling someone that they can't be relaxed about a 13% decline is
1/. Completely illogical.
2/. Ignorant.
I'm with Lou on this. I'm comfortable with my position. Not worried in the slightest.
Sirod69
bavarian girl ;-)
Unfortunately, I don't know if we already had this message?
Ramsundar K.• 2.MSc, MBA. PSPO. Innovation@Valeo | Technologist. Wannabe Strategist!
Testing a teleoperated/remote-driven car on a race track? Yes we do that too (use case 1) in the EU’s Horizon 2020 5GMED project https://5gmed.eu
Today all the 5GMED project teams are here at the Castelloli circuit for the technical review meetings and presentations of the current status of the WPs and four use case specific demos to the European Commission project office.
#Innovation @ Valeo and Valeo Deutschland. Our Mickael M. presented the UC1 slides, gave an quick intro to all the @Valeo sensors (lidar, radar, cameras, telematics control unit, etc) fitted in our Cruise4U car and then gave a demo together with Philippe Andrianavalona who was driving the car from the remote cockpit.
Tomorrow there will be the round two of demos and meeting at our cross border test site near Perthus.
Thanks to Francisco Vazquez Gallego Raül González José López Luque Jad Nasreddine Philippe Veyssiere David Porcuna Sanchez, Judit Bastida and all 5GMED partners for their presentations and project contributions.
Kevin Nguyen Mickael M. Alexander MARTENS Philippe Andrianavalona Aurélien M. Sophie de Lambert de Boisjean Philippe Seguret Emine Naouar Hervé MARCASUZAA
#ADAS #TCU #Telematics #5G #V2X
Ramsundar K.• 2.MSc, MBA. PSPO. Innovation@Valeo | Technologist. Wannabe Strategist!
Testing a teleoperated/remote-driven car on a race track? Yes we do that too (use case 1) in the EU’s Horizon 2020 5GMED project https://5gmed.eu
Today all the 5GMED project teams are here at the Castelloli circuit for the technical review meetings and presentations of the current status of the WPs and four use case specific demos to the European Commission project office.
#Innovation @ Valeo and Valeo Deutschland. Our Mickael M. presented the UC1 slides, gave an quick intro to all the @Valeo sensors (lidar, radar, cameras, telematics control unit, etc) fitted in our Cruise4U car and then gave a demo together with Philippe Andrianavalona who was driving the car from the remote cockpit.
Tomorrow there will be the round two of demos and meeting at our cross border test site near Perthus.
Thanks to Francisco Vazquez Gallego Raül González José López Luque Jad Nasreddine Philippe Veyssiere David Porcuna Sanchez, Judit Bastida and all 5GMED partners for their presentations and project contributions.
Kevin Nguyen Mickael M. Alexander MARTENS Philippe Andrianavalona Aurélien M. Sophie de Lambert de Boisjean Philippe Seguret Emine Naouar Hervé MARCASUZAA
#ADAS #TCU #Telematics #5G #V2X
robsmark
Regular
Rise from the ashes
Regular
Rise from the ashes
Regular
Wow this just gets even more weird
I wonder what the issue is? Snail mail would be even faster also walking to whatever state you live in and hand delivering my post to you would be faster. You replied jan 25 and I got the notification today
Sirod69
bavarian girl ;-)
DoD artificial intelligence agents successfully pilot fighter jet
- Published Feb. 13, 2023
AFRL’s Autonomous Air Combat Operations, or AACO, and DARPA’s Air Combat Evolution, or ACE, AI-driven autonomy agents piloted the U.S. Air Force Test Pilot School’s X-62A VISTA to perform advanced fighter maneuvers. AACO’s AI agents performed one-on-one beyond-visual-range, or BVR, engagements against a simulated adversary, and ACE’s AI agents performed within-visual-range maneuvering, known as dogfighting, against constructive AI red-team agents.
...............
AACO and ACE are autonomy programs focused on developing AI-driven autonomy for airborne tactical platforms. The goal of AACO is to develop and fly an advanced AI-driven autopilot capable of performing aviate and navigate functions and autonomous behaviors such as advanced intelligence, surveillance and reconnaissance and BVR combat.
DARPA’s ACE program aims to develop trusted, scalable, human-level, AI-driven autonomy for air combat by using human-machine collaborative dogfighting as its challenge problem.
Both programs recognize the value and need for the X-62 and similar testbeds, which are critical for the maturation of AI-driven autonomy capabilities and new uncrewed vehicle model designs, the experimentation lead said.
DoD artificial intelligence agents successfully pilot fighter jet
A joint Department of Defense team executed 12 flight tests in which artificial intelligence, or AI, agents piloted the X-62A Variable Stability In-Flight Simulator Test Aircraft, or VISTA, to perform
www.edwards.af.mil
The Pope
Regular
The yakster is back. Has he been too much of late on the hotcrapper digesting the shite from shareman and the Dean?
Maybe FF is there as well on long service leave from TSE. Just stirring FF as I know you can’t keep away from TSE and skim through (maybe read) all the posts on TSE. All the best on your sailing adventures on your yacht.
Rise from the ashes
Regular
Yes good to have him back, Release the Yakken!
Once in a blue
I'll check brn chat over there unfortunate for you ladies and gentlemen as my low quality posting takes place here now
28 when your lonely
Pocketchange321
Emerged
What happened? Why do I have to read through hundreds of pissing contests between grown people? Please keep it to relevant information regarding the company and the stock. If you participate in any of these one sentence jabs at others posters, you are the problem. Why can't we just have nice things. I totally understand why FF left, he saw this coming.
Yes good to have him back, Release the Yakken!
Once in a blue
Hello all. Yes back to watch all the various goings on happening here. And I have been having so much fun over at the Crapper silently annoying Shareman and Dean by modding as many of their posts as possible. They didn't even know I was around. blamed others! lol.
I wanted to Bait Shareman to confirm some info on his true identity which he obliged and I knew it would get me expelled, but it was worth seeing him panic first.
Its a dirty industry this Australian financial markets! absolutely corrupt. So glad I threw my licence at them some 20 yrs ago. Never regretted it.
Yak52
One thing I would like to know is what rate of success we have with IP negotiations. We have two confirmed successes (Renesas & Megachips), but how many failed ones?
In one podcast, Rob Telson jokingly said he needed a heart monitor during his negotiations, meaning it is a stressful process.
I’m still betting Akida 1500 will get us a higher score rate in IP negotiations.
In one podcast, Rob Telson jokingly said he needed a heart monitor during his negotiations, meaning it is a stressful process.
I’m still betting Akida 1500 will get us a higher score rate in IP negotiations.
Sirod69
bavarian girl ;-)
D
Deleted member 118
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FabricatedLunacy
Regular
Getupthere
Regular
Is Apple about to launch its own AI chatbot with Siri’s help? — The Standard
News that Apple is ramping up the power capacity at its data centres could signal that a Siri-powered search engine is on the way.
Getupthere
Regular
5 reasons MLops teams are using more edge ML — VentureBeat
As the number of machine learning (ML) use cases grows and evolves, an increasing number of MLops organizations are using more ML at the edge — that is, they are investing in running ML models on devices at the periphery of a network, including smart cameras, IoT computing devices, mobile...
As the number of machine learning (ML) use cases grows and evolves, an increasing number of MLops organizations are using more ML at the edge — that is, they are investing in running ML models on devices at the periphery of a network, including smart cameras, IoT computing devices, mobile devices or embedded systems.
ABI Research, a global technology intelligence firm, recently forecast that the edge ML enablement market will exceed $5 billion by 2027. While the market is still in a “nascent stage,” according to Lian Jye Su, research director at ABI Research, companies looking to ease the challenges of edge ML applications are turning to a variety of platforms, tools and solutions to boost an end-to-end MLops workflow.
“We are absolutely seeing MLops organizations increase the use of EdgeML,” said Lou Flynn, senior product manager for AI and analytics at SAS. “Enterprises big and small are running to the cloud for various reasons, but the cloud doesn’t lend itself to every use case. So organizations from nearly every industry, including aerospace, manufacturing, energy and automotive, leverage Edge AI to gain competitive advantage.”
Here are five reasons MLops teams are giving edge ML a thumbs-up:
1. Edge devices have become faster and more powerful.
“We have seen multiple companies focus on end-to-end processes around edge ML,” said Frederik Hvilshøj, lead ML engineer at data-centric computer vision company Encord. The two major reasons, he explained, are: Edge devices have become increasingly powerful while model compression has become more effective, which allows for running more powerful models at a higher speed; and edge devices also typically live much closer to the data source, which removes the necessity to move big volumes of data.
“The combination of the two means that high performance models can be run on edge devices at a close-to-real time speed,” he said. “Previously, GPUs living on central servers were necessary to get the high model throughput — but at the cost of having to transfer data back and forth, which made the use case less practical.”
2. Edge ML offers greater efficiency.
Today’s distributed data landscape is ripe with opportunity to analyze content to gain efficiencies, said Lou Flynn, senior product manager for AI and analytics at SAS.
“Many data sources originate from remote locations, such as a warehouse, a standalone sensor at a large agricultural site or even a CubeSat [a square-shaped miniature satellite] as part of a constellation of electro-optical imaging sensors,” he explained. “Each of these scenarios depicts use cases that could gain efficiencies by running edge ML vs. waiting for data to reconcile in cloud storage.”
3. Bandwidth and cost savings are key.
“You need to run ML models on the edge because of physics (bandwidth limitations, latency) and cost,” said Kjell Carlsson, head of data science strategy at Domino Data Lab. Carlsson explained that IoT is not feasible if data from every sensor needs to be streamed to the cloud to be analyzed.
“The network in a supermarket would not support the high-definition streaming from a couple dozen cameras, let alone the hundreds of cameras and other sensors you would want in a smart store,” he said. By running ML on the edge, you also avoid the cost of data transfer, he added.
“For example, a Fortune 500 manufacturer is using edge ML to continuously monitor equipment to predict equipment failure and alert staff to potential issues,” he said. “Using Domino’s MLops platform, they are monitoring 5,000+ signals with 150+ deep learning models.”
4. EdgeML helps scale the right data.
The real value of edge ML, said Hvilshøj, is that with distributed devices, you can scale your model inference without having to buy larger servers.
“With scaling inference out of the way, the next issue is collecting the right data for the next training iteration,” he said. In many cases, collecting raw data is not hard, but choosing data to label next becomes hard for large volumes of data. The compute resources on the edge devices can help identify what might be more relevant to label.
“For example, if the edge device is a phone and the user of the phone dismisses a prediction, this can be a good indicator that the model was wrong,” he said. “In turn, the particular piece of data would be good for retraining the model with proper labels.”
5. MLops organizations want more flexibility.
According to Flynn, MLops organizations should use their models to not only make better decisions, but to optimize these models for different hardware profiles — for example, using technology like the Apache TVM (Tensor Virtual Machine) to compile models to run more efficiently on different cloud providers and across devices with varying hardware (CPU, GPU and/or FPGAs). One SAS customer — Georgia-Pacific, an American pulp and paper company — uses edge computing at many of its remote manufacturing facilities where high-speed connectivity often isn’t reliable or cost-effective.
“This flexibility gives MLops teams agility to support a wide variety of use cases, enabling them to bring processing to their data on a growing pool of devices,” Flynn said. “While the range of devices are vast, they often come with resource limitations that could constrain model deployment. This is where model compression comes into play. Model compression reduces the footprint of the model and enables it to run on more compact devices (like an edge device) while improving the model’s computational performance.”
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