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Tothemoon24
Top 20
Smart Technology
TLME News Service
Published:17th Aug, 2023 at 7:17 PM
Gartner, Inc. today highlighted the top trends impacting the future of data science and machine learning (DSML) as the industry rapidly grows and evolves to meet the increasing significance of data in artificial intelligence (AI), particularly as the focus shifts towards generative AI investments.
Peter Krensky, Director Analyst at Gartner said: “As machine learning adoption continues to grow rapidly across industries, DSML is evolving from just focusing on predictive models, toward a more democratized, dynamic and data-centric discipline.
"This is now also fueled by the fervor around generative AI. While potential risks are emerging, so too are the many new capabilities and use cases for data scientists and their organizations.”
According to Gartner, the top trends shaping the future of DSML include:
Cloud Data Ecosystems
Data ecosystems are moving from self-contained software or blended deployments to full cloud-native solutions. By 2024, Gartner expects 50% of new system deployments in the cloud will be based on a cohesive cloud data ecosystem rather than on manually integrated point solutions.
Insight: Global Digital Transformation in Logistics
Gartner recommends organizations evaluate data ecosystems based on their ability to resolve distributed data challenges, as well as to access and integrate with data sources outside of their immediate environment.
Edge AI
Demand for Edge AI is growing to enable the processing of data at the point of creation at the edge, helping organizations to gain real-time insights, detect new patterns and meet stringent data privacy requirements.
Edge AI also helps organizations improve the development, orchestration, integration and deployment of AI.
Gartner predicts that more than 55% of all data analysis by deep neural networks will occur at the point of capture in an edge system by 2025, up from less than 10% in 2021.
Organizations should identify the applications, AI training and inferencing required to move to edge environments near IoT endpoints.
Responsible AI
Responsible AI makes AI a positive force, rather than a threat to society and to itself. It covers many aspects of making the right business and ethical choices when adopting AI that organizations often address independently, such as business and societal value, risk, trust, transparency and accountability.
Gartner predicts the concentration of pretrained AI models among 1% of AI vendors by 2025 will make responsible AI a societal concern.
Gartner recommends organizations adopt a risk-proportional approach to deliver AI value and take caution when applying solutions and models.
Seek assurances from vendors to ensure they are managing their risk and compliance obligations, protecting organizations from potential financial loss, legal action and reputational damage.
Data-Centric AI
Data-centric AI represents a shift from a model and code-centric approach to being more data focused to build better AI systems.
Solutions such as AI-specific data management, synthetic data and data labeling technologies, aim to solve many data challenges, including accessibility, volume, privacy, security, complexity and scope.
The use of generative AI to create synthetic data is one area that is rapidly growing, relieving the burden of obtaining real-world data so machine learning models can be trained effectively.
By 2024, Gartner predicts 60% of data for AI will be synthetic to simulate reality, future scenarios and derisk AI, up from 1% in 2021.
Accelerated AI Investment
Investment in AI will continue to accelerate by organizations implementing solutions, as well as by industries looking to grow through AI technologies and AI-based businesses.
By the end of 2026, Gartner predicts that more than $10 billion will have been invested in AI startups that rely on foundation models – large AI models trained on huge amounts of data.
A recent Gartner poll of more than 2,500 executive leaders found that 45% reported that recent hype around ChatGPT prompted them to increase AI investments.
70% said their organization is in investigation and exploration mode with generative AI, while 19% are in pilot or production mode.
Top Trends Shaping the Future of Data Science and Machine Learning
TLME News Service
Published:17th Aug, 2023 at 7:17 PM
Gartner, Inc. today highlighted the top trends impacting the future of data science and machine learning (DSML) as the industry rapidly grows and evolves to meet the increasing significance of data in artificial intelligence (AI), particularly as the focus shifts towards generative AI investments.
Peter Krensky, Director Analyst at Gartner said: “As machine learning adoption continues to grow rapidly across industries, DSML is evolving from just focusing on predictive models, toward a more democratized, dynamic and data-centric discipline.
"This is now also fueled by the fervor around generative AI. While potential risks are emerging, so too are the many new capabilities and use cases for data scientists and their organizations.”
According to Gartner, the top trends shaping the future of DSML include:
Cloud Data Ecosystems
Data ecosystems are moving from self-contained software or blended deployments to full cloud-native solutions. By 2024, Gartner expects 50% of new system deployments in the cloud will be based on a cohesive cloud data ecosystem rather than on manually integrated point solutions.
Insight: Global Digital Transformation in Logistics
Gartner recommends organizations evaluate data ecosystems based on their ability to resolve distributed data challenges, as well as to access and integrate with data sources outside of their immediate environment.
Edge AI
Demand for Edge AI is growing to enable the processing of data at the point of creation at the edge, helping organizations to gain real-time insights, detect new patterns and meet stringent data privacy requirements.
Edge AI also helps organizations improve the development, orchestration, integration and deployment of AI.
Gartner predicts that more than 55% of all data analysis by deep neural networks will occur at the point of capture in an edge system by 2025, up from less than 10% in 2021.
Organizations should identify the applications, AI training and inferencing required to move to edge environments near IoT endpoints.
Responsible AI
Responsible AI makes AI a positive force, rather than a threat to society and to itself. It covers many aspects of making the right business and ethical choices when adopting AI that organizations often address independently, such as business and societal value, risk, trust, transparency and accountability.
Gartner predicts the concentration of pretrained AI models among 1% of AI vendors by 2025 will make responsible AI a societal concern.
Gartner recommends organizations adopt a risk-proportional approach to deliver AI value and take caution when applying solutions and models.
Seek assurances from vendors to ensure they are managing their risk and compliance obligations, protecting organizations from potential financial loss, legal action and reputational damage.
Data-Centric AI
Data-centric AI represents a shift from a model and code-centric approach to being more data focused to build better AI systems.
Solutions such as AI-specific data management, synthetic data and data labeling technologies, aim to solve many data challenges, including accessibility, volume, privacy, security, complexity and scope.
The use of generative AI to create synthetic data is one area that is rapidly growing, relieving the burden of obtaining real-world data so machine learning models can be trained effectively.
By 2024, Gartner predicts 60% of data for AI will be synthetic to simulate reality, future scenarios and derisk AI, up from 1% in 2021.
Accelerated AI Investment
Investment in AI will continue to accelerate by organizations implementing solutions, as well as by industries looking to grow through AI technologies and AI-based businesses.
By the end of 2026, Gartner predicts that more than $10 billion will have been invested in AI startups that rely on foundation models – large AI models trained on huge amounts of data.
A recent Gartner poll of more than 2,500 executive leaders found that 45% reported that recent hype around ChatGPT prompted them to increase AI investments.
70% said their organization is in investigation and exploration mode with generative AI, while 19% are in pilot or production mode.
Easytiger
Regular
Cartagena
Regular
The company (Renesas) says it is developing a new series of its RA MCUs based on the Cortex-M85 processor, planned for release in 2023.
Attachments
cosors
👀
What a beautiful family member! And they are almost always the only sensible ones in the family.) with the heart fully with the pack and are always honest. And just this magnificent specimen! How can you not fall for this look...?
'Mine' had made me a better person/human I think.
Fullmoonfever
Top 20
Whilst only a small mention, great to see confirmation we're still in the mix with some heavy hitters over at NASA & Ames Research Centre.
Gotta say something about the tech
The original neuromorphic flight "test" was with Loihi and the TechEd Sat from what I read but states moving to next phases with more neuromorphic.
BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite
Date Acquired
August 2, 2023
Publication Date
August 11, 2023
Publication Information
Publication: SmallSat Conference Proceedings
Part of the conclusion...
Building on earlier successes with GPUs, and more recently, a neuromorphic processor flight test, the notion of a collaborative BrainStack orbital AI/ML
laboratory module is presented. The intention is to be able to perform experiments on multiple hardware and
software AI/ML elements on the same flight with different collaborative teams.
The TES-n Common AI/ML Software Interface will permit a menu driven set
of experiments across individual elements. The next set of TES BrainStack experiments will host combinations
of GPUs and neuromorphic processors, with flexibility to support upcoming novel systems and their unique interfaces. Such a collaborative BrainStack system will greatly expand the use of these remarkable new tools and methods in the space sector.
Full paper:
HERE
Gotta say something about the tech
The original neuromorphic flight "test" was with Loihi and the TechEd Sat from what I read but states moving to next phases with more neuromorphic.
BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite
Date Acquired
August 2, 2023
Publication Date
August 11, 2023
Publication Information
Publication: SmallSat Conference Proceedings
Part of the conclusion...
Building on earlier successes with GPUs, and more recently, a neuromorphic processor flight test, the notion of a collaborative BrainStack orbital AI/ML
laboratory module is presented. The intention is to be able to perform experiments on multiple hardware and
software AI/ML elements on the same flight with different collaborative teams.
The TES-n Common AI/ML Software Interface will permit a menu driven set
of experiments across individual elements. The next set of TES BrainStack experiments will host combinations
of GPUs and neuromorphic processors, with flexibility to support upcoming novel systems and their unique interfaces. Such a collaborative BrainStack system will greatly expand the use of these remarkable new tools and methods in the space sector.
Full paper:
HERE
More on Renesas collaborating with AMD
Learning
Learning to the Top 🕵♂️
AI Chips for Edge Applications 2024-2034: Artificial Intelligence at the Edge
For more information, visit https://www.idtechex.com/en/research-report/ai-chips-for-edge-applications-2024-2034-artificial-intelligence-at-the-edge/956.
www.edge-ai-vision.com
Learning 🏖
Dallas
Regular
SorryView attachment 42359
AI Chips for Edge Applications 2024-2034: Artificial Intelligence at the Edge
For more information, visit https://www.idtechex.com/en/research-report/ai-chips-for-edge-applications-2024-2034-artificial-intelligence-at-the-edge/956.
Lernen 🏖
robsmark
Regular
No idea what this is about but do recall
akida/smile · Datasets at Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.huggingface.co
Rob liked it?! On LinkedIn?!
jtardif999
Regular
Rubbish and you are grandstanding
robsmark
Regular
Yes mate, Everytime Rob likes something new I purchase more shares
I'm taking the Aussie Shiraz of course
Brainchip should replace him with one of these and save $300k+ a year.
Yes the way things are going BRN is definitely not a potential investment opportunity so you have rightfully been modded. By the way Aperol Spritz is my new favourite drinkLolz got moderated for posting a chartdamn A.i modbot on the fritz. I never posted a chart.
Reason: The post contains a chart with financial information and implies a potential investment opportunity. This violates the rule against providing licensed financial advice in Australia.
SERA2g
Founding Member
It says we are analog and that TrueNorth is the first commercial neuromorphic chip.
Not great research lol.
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