20240411 QFM009 Machine Intelligence Reading List March 2024Matthew Sinclair
The document provides a summary of topics related to machine intelligence that were discussed in March 2024, including NVIDIA's Project GR00T which aims to create a general-purpose foundation model for humanoid robots, DeepMind's SIMA which explores using generative AI in 3D virtual environments, Meta's development of large AI clusters to support advanced model training, and an open-source desktop tool for interacting with large language models. The summary also mentions articles on understanding the abilities of large language models, security concerns regarding AI metacognition, and innovative defense strategies against AI attacks.
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Produced by Nathan Benaich and Air Street Capital team
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
State of AI Report 2023 - ONLINE presentationssuser2750ef
State of AI Report 2023 - ONLINE.pptx
When conducting a PEST analysis for the Syrian conflict, it's important to consider the political, economic, socio-cultural, and technological factors that have influenced and continue to impact the situation in Syria. Here's a high-level overview of a PEST analysis for the Syrian conflict:
1. Political Factors:
- Government Instability: Ongoing civil war and conflict have led to political instability and a complex power struggle between various factions and international players.
- Foreign Intervention: Involvement of external powers and regional actors has exacerbated the conflict and added geopolitical complexities to the situation.
- International Relations: Relations with global powers like the United States, Russia, and regional players like Iran and Turkey significantly impact the conflict dynamics.
2. Economic Factors:
- Humanitarian Crisis: The conflict has resulted in a severe humanitarian crisis, causing widespread displacement, destruction of infrastructure, and economic decline.
- Sanctions and Trade Barriers: International sanctions and disrupted trade have further worsened the economic situation in Syria, affecting the livelihoods of the population.
- Resource Depletion: Conflict-driven resource depletion, including loss of agricultural lands and disruption of industries, has weakened the economy.
3. Socio-cultural Factors:
- Civilian Suffering: The conflict has led to a significant loss of life, displacement of populations, and severe trauma among civilians, impacting social cohesion and community structures.
- Ethnic and Religious Divisions: Deep-seated ethnic and religious divisions have fueled the conflict, leading to sectarian tensions and societal fragmentation.
- Refugee Crisis: The conflict has triggered a massive refugee crisis, with millions of Syrians seeking asylum in neighboring countries and beyond, straining regional stability.
4. Technological Factors:
- Communication and Propaganda: Technology, including social media, has been used for communication, mobilization, and spreading propaganda by various actors in the conflict.
- Warfare Technology: Advancements in warfare technology and the use of drones, cyber warfare, and other advanced weaponry have transformed the nature of conflict in Syria.
- Cybersecurity Concerns: The conflict has also raised concerns about cybersecurity threats, misinformation campaigns, and digital vulnerabilities in the region.
This analysis provides a broad understanding of the multifaceted nature of the Syrian conflict, highlighting the diverse factors at play and the complex challenges facing Syria and the international community.
Copy of State of AI Report 2023 - ONLINE.pptxmpower4ru
The document provides an overview and summary of the 2023 State of AI Report produced by Nathan Benaich and the Air Street Capital team. It discusses key dimensions covered in the report including research, industry, politics, safety, and predictions. In the research section, it summarizes progress made in large language models, diffusion models, multimodality, and applications in life sciences. The industry section summarizes growth in the AI sector, demand for GPUs, and investments in generative AI applications. The politics section discusses regulatory approaches and geopolitics around AI and chips. It also includes a scorecard reviewing predictions made in the 2022 report.
20240414 QFM012 Irresponsible AI Reading List March 2024Matthew Sinclair
This month's Quantum Fax Machine: Irresponsible AI Reading List explores themes around AI technology including cybersecurity, digital deception, and the societal implications of AI. Articles discuss topics such as using an AI clone to attend meetings, vulnerabilities in large language models, manipulating AI with ASCII art, AI voice cloning scams, declining public trust in AI, and challenges of authentic human interactions online amidst generative AI content. The list aims to provide a thought-provoking roundup of issues at the intersection of technology, ethics, and society.
Chat GPT 4 can pass the American state bar exam, but before you go expecting to see robot lawyers taking over the courtroom, hold your horses cowboys – we're not quite there yet. That being said, AI is becoming increasingly more human-like, and as a VC we need to start thinking about how this new wave of technology is going to affect the way we build and run businesses. What do we need to do differently? How can we make sure that our investment strategies are reflecting these changes? It's a brave new world out there, and we’ve got to keep the big picture in mind!
Sharing here with you what we at Cavalry Ventures found out during our Generative AI deep dive.
20240411 QFM009 Machine Intelligence Reading List March 2024Matthew Sinclair
The document provides a summary of topics related to machine intelligence that were discussed in March 2024, including NVIDIA's Project GR00T which aims to create a general-purpose foundation model for humanoid robots, DeepMind's SIMA which explores using generative AI in 3D virtual environments, Meta's development of large AI clusters to support advanced model training, and an open-source desktop tool for interacting with large language models. The summary also mentions articles on understanding the abilities of large language models, security concerns regarding AI metacognition, and innovative defense strategies against AI attacks.
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Produced by Nathan Benaich and Air Street Capital team
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
State of AI Report 2023 - ONLINE presentationssuser2750ef
State of AI Report 2023 - ONLINE.pptx
When conducting a PEST analysis for the Syrian conflict, it's important to consider the political, economic, socio-cultural, and technological factors that have influenced and continue to impact the situation in Syria. Here's a high-level overview of a PEST analysis for the Syrian conflict:
1. Political Factors:
- Government Instability: Ongoing civil war and conflict have led to political instability and a complex power struggle between various factions and international players.
- Foreign Intervention: Involvement of external powers and regional actors has exacerbated the conflict and added geopolitical complexities to the situation.
- International Relations: Relations with global powers like the United States, Russia, and regional players like Iran and Turkey significantly impact the conflict dynamics.
2. Economic Factors:
- Humanitarian Crisis: The conflict has resulted in a severe humanitarian crisis, causing widespread displacement, destruction of infrastructure, and economic decline.
- Sanctions and Trade Barriers: International sanctions and disrupted trade have further worsened the economic situation in Syria, affecting the livelihoods of the population.
- Resource Depletion: Conflict-driven resource depletion, including loss of agricultural lands and disruption of industries, has weakened the economy.
3. Socio-cultural Factors:
- Civilian Suffering: The conflict has led to a significant loss of life, displacement of populations, and severe trauma among civilians, impacting social cohesion and community structures.
- Ethnic and Religious Divisions: Deep-seated ethnic and religious divisions have fueled the conflict, leading to sectarian tensions and societal fragmentation.
- Refugee Crisis: The conflict has triggered a massive refugee crisis, with millions of Syrians seeking asylum in neighboring countries and beyond, straining regional stability.
4. Technological Factors:
- Communication and Propaganda: Technology, including social media, has been used for communication, mobilization, and spreading propaganda by various actors in the conflict.
- Warfare Technology: Advancements in warfare technology and the use of drones, cyber warfare, and other advanced weaponry have transformed the nature of conflict in Syria.
- Cybersecurity Concerns: The conflict has also raised concerns about cybersecurity threats, misinformation campaigns, and digital vulnerabilities in the region.
This analysis provides a broad understanding of the multifaceted nature of the Syrian conflict, highlighting the diverse factors at play and the complex challenges facing Syria and the international community.
Copy of State of AI Report 2023 - ONLINE.pptxmpower4ru
The document provides an overview and summary of the 2023 State of AI Report produced by Nathan Benaich and the Air Street Capital team. It discusses key dimensions covered in the report including research, industry, politics, safety, and predictions. In the research section, it summarizes progress made in large language models, diffusion models, multimodality, and applications in life sciences. The industry section summarizes growth in the AI sector, demand for GPUs, and investments in generative AI applications. The politics section discusses regulatory approaches and geopolitics around AI and chips. It also includes a scorecard reviewing predictions made in the 2022 report.
20240414 QFM012 Irresponsible AI Reading List March 2024Matthew Sinclair
This month's Quantum Fax Machine: Irresponsible AI Reading List explores themes around AI technology including cybersecurity, digital deception, and the societal implications of AI. Articles discuss topics such as using an AI clone to attend meetings, vulnerabilities in large language models, manipulating AI with ASCII art, AI voice cloning scams, declining public trust in AI, and challenges of authentic human interactions online amidst generative AI content. The list aims to provide a thought-provoking roundup of issues at the intersection of technology, ethics, and society.
Chat GPT 4 can pass the American state bar exam, but before you go expecting to see robot lawyers taking over the courtroom, hold your horses cowboys – we're not quite there yet. That being said, AI is becoming increasingly more human-like, and as a VC we need to start thinking about how this new wave of technology is going to affect the way we build and run businesses. What do we need to do differently? How can we make sure that our investment strategies are reflecting these changes? It's a brave new world out there, and we’ve got to keep the big picture in mind!
Sharing here with you what we at Cavalry Ventures found out during our Generative AI deep dive.
This document discusses generative AI, including what it is, how it works, challenges, and potential business uses. Some key points:
- Generative AI can automatically generate new text, images, videos and other content based on training data, rather than just categorizing data like other machine learning.
- It uses large language models trained on vast datasets to generate human-like responses to prompts. While this allows for many potential business uses, challenges include lack of transparency, privacy/security issues, and the risk of factual inaccuracies.
- Generative AI could be used by businesses for tasks like document processing, writing code, augmenting human work, and creating marketing content. Industries like insurance, legal,
Delve into this insightful article to explore the current state of generative AI, its ethical implications, and the power of generative AI models across various industries.
The coming generative AI trends of 2024.pdfSoluLab1231
Generative AI, short for Generative Artificial Intelligence, is a subfield of Artificial Intelligence that focuses on developing algorithms and models capable of generating new, original content. Unlike traditional AI systems that are rule-based and task-specific, generative AI possesses the ability to autonomously produce content, ranging from text and images to audio and video.
At the heart of generative AI are advanced machine learning techniques, particularly deep learning. Generative models, a category of models within the realm of generative AI, are designed to understand and replicate patterns in data, allowing them to create output that closely resembles human-generated content.
Generative AI systems learn from vast datasets to understand the underlying structures and features present in the data. Once trained, these systems can generate new content by extrapolating from the patterns they’ve learned. This capability is particularly powerful in tasks such as image synthesis, text generation, and even the creation of multimedia content.
A recap of interesting points and quotes from the May 2024 WSO2CON opensource application development conference. Focuses primarily on keynotes and panel sessions.
Introduction to Artificial Intelligence.pptxRSAISHANKAR
My name is R. Sai Shankar. In here, I'm publish a small PowerPoint Presentation on Artificial Intelligence. Here is the link for my YouTube Channel "Learn AI With Shankar". Please Like Share Subscribe. Thank you.
https://youtu.be/3N5C99sb-gc
What is Artificial Intelligence?
Where is the value potential of AI?
Major Acquisitions in AI
AI business cases
AI (& BI) Ecosystem
AI challenges
Networking/expertise
Conclusion
[DSC Europe 23] Shahab Anbarjafari - Generative AI: Impact of Responsible AIDataScienceConferenc1
Today, we embark on a journey into the realm of Generative AI (Gen AI), a force of innovation and possibility. We'll not only unveil the vast opportunities it offers but also confront the ethical challenges it poses. In the spirit of responsible innovation, we'll then dive deep into Responsible AI, illuminating the path to its implementation in this era of Gen AI. Join us for a profound exploration of this technological frontier, where our commitment to responsibility and foresight shapes the future.
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfHermes Romero
The document provides an overview of generative AI, including its key concepts and applications. It discusses transformer models versus neural networks, explaining that transformer models use self-attention to capture long-range dependencies in sequential data like text. Large language models (LLMs) based on the transformer architecture have shown strong performance in natural language generation tasks. The document outlines the evolution of generative AI techniques from early machine learning to modern large pretrained models. It also surveys some commercial generative AI applications in industries like healthcare, finance, and gaming.
AI and ML Series - Introduction to Generative AI and LLMs - Session 1DianaGray10
Session 1
👉This first session will cover an introduction to Generative AI & harnessing the power of large language models. The following topics will be discussed:
Introduction to Generative AI & harnessing the power of large language models.
What’s generative AI & what’s LLM.
How are we using it in our document understanding & communication mining models?
How to develop a trustworthy and unbiased AI model using LLM & GenAI.
Personal Intelligent Assistant
Speakers:
📌George Roth - AI Evangelist at UiPath
📌Sharon Palawandram - Senior Machine Learning Consultant @ Ashling Partners & UiPath MVP
📌Russel Alfeche - Technology Leader RPA @qBotica & UiPath MVP
Will artificial intelligence replace programmersMaciej Dziergwa
Artificial intelligence can compose songs, paint pictures, help in cancer therapy, drive cars and play games. It’s also starting to write code.
Does it mean that the days of human programmers are already numbered? Will software engineering be automated?
While technological advances say they are on the brink of achieving that perfect artificial intelligence, we are not quite there yet. Fortunately for us, an AI does not need to be irreproachable, just better than a human. Take connected cars, for instance. An AI-based driver may not be mistake-proof, but it is certainly less imperfect than a human driver.
This is very much the case in cybersecurity where IT experts are changing the rules of the game using Machine Learning.
In the landscape of technological evolution, Generative Artificial Intelligence stands at the forefront, reshaping our interactions with technology, creativity, and the world at large. As we teeter on the brink of a new era, the trajectory of Generative AI promises to redefine industries, reshape human experiences, and unlock unprecedented possibilities.
Generative AI's Ascendance:
Empowered by advanced machine learning techniques, Generative AI possesses the remarkable ability to create, innovate, and simulate, once thought to be exclusive to human intellect. Deep learning, anchored in neural networks and algorithms, has paved the way for machines not only to comprehend but also autonomously generate content.
1. Enhancing efficiency by automating repetitive tasks, reducing costs, and saving time. Generative AI models can generate content like text, images, videos, and code much faster than humans.
2. Enabling personalization at scale by understanding individual customer needs and preferences and delivering hyper-personalized experiences. Generative AI can create customized products and services.
3. Fostering
Artificial intelligence for Engineers unit1SURBHI SAROHA
The document discusses the evolution and approaches of artificial intelligence (AI). It begins with a brief history of AI from its origins in 1956 to recent decades where machine learning has been applied successfully. Four main approaches to AI are described: reactive machines, limited memory, theory of mind, and self-awareness. The document provides examples of each approach. It also discusses what skills and topics engineers should understand to work in AI, such as programming languages, algorithms, and deep learning. Emerging technologies related to AI like robotics, blockchain, and cybersecurity are outlined. Finally, some key ethical concerns regarding AI like job loss, imperfect systems, and bias are listed.
Artificial intelligence, machine learning, and deep learning are related concepts in the field of artificial intelligence. Machine learning is a subset of AI that uses algorithms to learn from data and make predictions without being explicitly programmed, while deep learning is a specific type of machine learning that uses neural networks. The document provides definitions and examples of these concepts to help explain the differences between them.
This whitepaper provides an overview of artificial intelligence (AI) and its commercialization. It discusses the history and development of AI from early pattern recognition (AI 1.0) to today's deep learning (AI 2.0) to the emerging contextual reasoning (AI 3.0). Key points include how transfer learning and increased computing power are driving new AI applications and how AI is being applied commercially in healthcare, manufacturing, logistics, and other industries. The document also addresses the global demand for AI talent and the challenges of developing reliable AI systems that can operate under changing conditions.
Understanding the New World of Cognitive ComputingDATAVERSITY
Cognitive Computing is a rapidly developing technology that has reached practical application and implementation. So what is it? Do you need it? How can it benefit your business?
In this webinar a panel of experts in Cognitive Computing will discuss the technology, the current practical applications, and where this technology is going. The discussion will start with a review of a recent survey produced by DATAVERSITY on how Cognitive Computing is currently understood by your peers. The panel will also review many components of the technology including:
Cognitive Analytics
Machine Learning
Deep Learning
Reasoning
And next generation artificial intelligence (AI)
And get involved in the discussion with your own questions to present to the panel.
This document discusses generative AI, including what it is, how it works, challenges, and potential business uses. Some key points:
- Generative AI can automatically generate new text, images, videos and other content based on training data, rather than just categorizing data like other machine learning.
- It uses large language models trained on vast datasets to generate human-like responses to prompts. While this allows for many potential business uses, challenges include lack of transparency, privacy/security issues, and the risk of factual inaccuracies.
- Generative AI could be used by businesses for tasks like document processing, writing code, augmenting human work, and creating marketing content. Industries like insurance, legal,
Delve into this insightful article to explore the current state of generative AI, its ethical implications, and the power of generative AI models across various industries.
The coming generative AI trends of 2024.pdfSoluLab1231
Generative AI, short for Generative Artificial Intelligence, is a subfield of Artificial Intelligence that focuses on developing algorithms and models capable of generating new, original content. Unlike traditional AI systems that are rule-based and task-specific, generative AI possesses the ability to autonomously produce content, ranging from text and images to audio and video.
At the heart of generative AI are advanced machine learning techniques, particularly deep learning. Generative models, a category of models within the realm of generative AI, are designed to understand and replicate patterns in data, allowing them to create output that closely resembles human-generated content.
Generative AI systems learn from vast datasets to understand the underlying structures and features present in the data. Once trained, these systems can generate new content by extrapolating from the patterns they’ve learned. This capability is particularly powerful in tasks such as image synthesis, text generation, and even the creation of multimedia content.
A recap of interesting points and quotes from the May 2024 WSO2CON opensource application development conference. Focuses primarily on keynotes and panel sessions.
Introduction to Artificial Intelligence.pptxRSAISHANKAR
My name is R. Sai Shankar. In here, I'm publish a small PowerPoint Presentation on Artificial Intelligence. Here is the link for my YouTube Channel "Learn AI With Shankar". Please Like Share Subscribe. Thank you.
https://youtu.be/3N5C99sb-gc
What is Artificial Intelligence?
Where is the value potential of AI?
Major Acquisitions in AI
AI business cases
AI (& BI) Ecosystem
AI challenges
Networking/expertise
Conclusion
[DSC Europe 23] Shahab Anbarjafari - Generative AI: Impact of Responsible AIDataScienceConferenc1
Today, we embark on a journey into the realm of Generative AI (Gen AI), a force of innovation and possibility. We'll not only unveil the vast opportunities it offers but also confront the ethical challenges it poses. In the spirit of responsible innovation, we'll then dive deep into Responsible AI, illuminating the path to its implementation in this era of Gen AI. Join us for a profound exploration of this technological frontier, where our commitment to responsibility and foresight shapes the future.
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfHermes Romero
The document provides an overview of generative AI, including its key concepts and applications. It discusses transformer models versus neural networks, explaining that transformer models use self-attention to capture long-range dependencies in sequential data like text. Large language models (LLMs) based on the transformer architecture have shown strong performance in natural language generation tasks. The document outlines the evolution of generative AI techniques from early machine learning to modern large pretrained models. It also surveys some commercial generative AI applications in industries like healthcare, finance, and gaming.
AI and ML Series - Introduction to Generative AI and LLMs - Session 1DianaGray10
Session 1
👉This first session will cover an introduction to Generative AI & harnessing the power of large language models. The following topics will be discussed:
Introduction to Generative AI & harnessing the power of large language models.
What’s generative AI & what’s LLM.
How are we using it in our document understanding & communication mining models?
How to develop a trustworthy and unbiased AI model using LLM & GenAI.
Personal Intelligent Assistant
Speakers:
📌George Roth - AI Evangelist at UiPath
📌Sharon Palawandram - Senior Machine Learning Consultant @ Ashling Partners & UiPath MVP
📌Russel Alfeche - Technology Leader RPA @qBotica & UiPath MVP
Will artificial intelligence replace programmersMaciej Dziergwa
Artificial intelligence can compose songs, paint pictures, help in cancer therapy, drive cars and play games. It’s also starting to write code.
Does it mean that the days of human programmers are already numbered? Will software engineering be automated?
While technological advances say they are on the brink of achieving that perfect artificial intelligence, we are not quite there yet. Fortunately for us, an AI does not need to be irreproachable, just better than a human. Take connected cars, for instance. An AI-based driver may not be mistake-proof, but it is certainly less imperfect than a human driver.
This is very much the case in cybersecurity where IT experts are changing the rules of the game using Machine Learning.
In the landscape of technological evolution, Generative Artificial Intelligence stands at the forefront, reshaping our interactions with technology, creativity, and the world at large. As we teeter on the brink of a new era, the trajectory of Generative AI promises to redefine industries, reshape human experiences, and unlock unprecedented possibilities.
Generative AI's Ascendance:
Empowered by advanced machine learning techniques, Generative AI possesses the remarkable ability to create, innovate, and simulate, once thought to be exclusive to human intellect. Deep learning, anchored in neural networks and algorithms, has paved the way for machines not only to comprehend but also autonomously generate content.
1. Enhancing efficiency by automating repetitive tasks, reducing costs, and saving time. Generative AI models can generate content like text, images, videos, and code much faster than humans.
2. Enabling personalization at scale by understanding individual customer needs and preferences and delivering hyper-personalized experiences. Generative AI can create customized products and services.
3. Fostering
Artificial intelligence for Engineers unit1SURBHI SAROHA
The document discusses the evolution and approaches of artificial intelligence (AI). It begins with a brief history of AI from its origins in 1956 to recent decades where machine learning has been applied successfully. Four main approaches to AI are described: reactive machines, limited memory, theory of mind, and self-awareness. The document provides examples of each approach. It also discusses what skills and topics engineers should understand to work in AI, such as programming languages, algorithms, and deep learning. Emerging technologies related to AI like robotics, blockchain, and cybersecurity are outlined. Finally, some key ethical concerns regarding AI like job loss, imperfect systems, and bias are listed.
Artificial intelligence, machine learning, and deep learning are related concepts in the field of artificial intelligence. Machine learning is a subset of AI that uses algorithms to learn from data and make predictions without being explicitly programmed, while deep learning is a specific type of machine learning that uses neural networks. The document provides definitions and examples of these concepts to help explain the differences between them.
This whitepaper provides an overview of artificial intelligence (AI) and its commercialization. It discusses the history and development of AI from early pattern recognition (AI 1.0) to today's deep learning (AI 2.0) to the emerging contextual reasoning (AI 3.0). Key points include how transfer learning and increased computing power are driving new AI applications and how AI is being applied commercially in healthcare, manufacturing, logistics, and other industries. The document also addresses the global demand for AI talent and the challenges of developing reliable AI systems that can operate under changing conditions.
Understanding the New World of Cognitive ComputingDATAVERSITY
Cognitive Computing is a rapidly developing technology that has reached practical application and implementation. So what is it? Do you need it? How can it benefit your business?
In this webinar a panel of experts in Cognitive Computing will discuss the technology, the current practical applications, and where this technology is going. The discussion will start with a review of a recent survey produced by DATAVERSITY on how Cognitive Computing is currently understood by your peers. The panel will also review many components of the technology including:
Cognitive Analytics
Machine Learning
Deep Learning
Reasoning
And next generation artificial intelligence (AI)
And get involved in the discussion with your own questions to present to the panel.
20240413 QFM011 Engineering Leadership Reading List March 2024Matthew Sinclair
The document is a reading list from the Quantum Fax Machine's March 2024 edition of Engineering Leadership. It provides summaries and tags for 11 articles on topics related to engineering leadership, startups, meetings, power dynamics, pacing, venture studios, product prioritization, workplace statistics, and more. Key themes include the importance of transparency, balancing team performance and well-being, leveraging engineering expertise, and addressing employee engagement.
The document provides a summary of articles and resources related to the Elixir programming ecosystem from March 2024. It discusses tools and libraries for enhancing code readability with Doctest Formatter, managing environment configurations without dependencies, using GenServer for concurrency, contrasting Phoenix and Rails architectures, improving error handling, integrating with large language models through instructor_ex, secure coding practices with Semgrep, optimizing code quality with Credo, building GraphQL APIs with Absinthe and Phoenix, and implementing conversational agents with Elixir. The summary also includes relevant hashtags for each topic.
This is a quick summary along with a few synthesised insights from the FinovateEurope 2024 London conference. The deck includes a 1-page summary for each of the 37 fintech demos presented on Day 1 (27th February).
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
2. QFM005: Machine Intelligence
Reading List February 2024
Everything that I found interesting last month about machines behaving
intelligently
This month's reading highlights a recurring theme in the ethical implications and
societal impact of machine intelligence such as Marcin Jabłonowski's clever
exploration of AI avatars in Marcin 2.0, the discourse on the replacement of
human jobs by AI at Klarna, and Geoffrey Hinton's discussion on the potential
future dangers of AI at scale.
Going a bit deeper into practical advances in LLM tech we see the introduction of
Mamba, a State Space Model challenging Transformer models, and the innovative
approaches to AI safety and effectiveness in GradSafe and Matryoshka
Embedding Models. We also explore the potential of AI to replace human jobs and
the ethical considerations this introduces as well as advancements in AI safety
and model efficiency.
Perhaps the most incredible generative AI release this month was OpenAI's SORA
video generation. The potential for SORA to disrupt video creation and production
are obvious and profound, but an intelligent system that has an understanding of
real-world physics has much wider implications.
Enjoy!
Key:
: Mentions technology
: Talks about technology in real-world use cases
: Talks about details of machine intelligence technologies
: Using and working with machine intelligence technologies in software
: Programming new machine intelligence concepts and
implementations
Source: Photo by Mike Kononov on Unsplash
2
3. Marcin 2.0 is Marcin Jabłonowski's experiment with a Personal Digital
Twin This video introduces Marcin 2.0, a project
showcasing the advancements and potential of AI-avatars,
highlighting their impressive capabilities in transforming
communication across different languages and cultures, while also
addressing the ethical considerations and cybersecurity threats that
come with integrating advanced AI into our lives. Watch to the end.
There is a clever twist.
#DigitalTwin #AIAvatars #EthicalAI #Cybersecurity
#FutureOfAI
Insight
!
There is a lot of talk in the article below, and more generally, about the
"efficiency of LLMs" with respect to how many humans can be replaced
with the use of these technologies. This worries me greatly because the
wholesale replacement of humans is not going to lead us to utopia. What I
think the debate is missing is the tradeoff between efficiency and
experience. If we make systems more efficient, but neglect to consider
the impact that efficiency has on customers and staff, we may end up
with some very poor outcomes. It is also worth noting that in the days
after the Klarna announcement there have been discussions about how
good their new chatbot really is and perhaps more importantly, how many
of the 700 laid off were in roles replaced by the chatbot.
3
4. Klarna says its AI assistant does the work
of 700 people after it laid off 700 people:
Klarna's OpenAI-powered virtual
assistant now handles two-thirds of
customer service chats, equating to the
workload of 700 humans, showcasing
significant efficiency gains and potential
profit improvement for the company.
#Klarna #OpenAI #VirtualAssistant
#CustomerService #AIInnovation
4
5. Mamba Explained: The State Space Model
taking on Transformers: The
article discusses Mamba, a State Space
Model (SSM) that challenges the
dominance of Transformer models in AI by
offering similar performance with faster
processing and better scalability for long
sequences. Mamba optimises efficiency
and effectiveness, promising
advancements in AI safety, interpretability,
and applications across various modalities.
#MambaAI #StateSpaceModels
#AIInnovation #EfficientAI
#LongSequenceModeling
5
6. Is AI Actually Useful?: This video
examines a recent Harvard Business
Review paper "Navigating the Jagged
Technological Frontier" and explores the
implications of the paper's findings on the
use of generative AI in professional
knowledge work environments.
#AIProductivity #ChatGPTImpact
#FutureOfWork #AIinConsulting
#BusinessInnovation
6
7. Romanes Lecture: ‘Godfather of AI’ speaks
about the risks of artificial intelligence:
In his Romanes Lecture at the
University of Oxford, Geoffrey Hinton,
known as the 'Godfather of AI,' discussed
the potential dangers of AI, including its
ability to replace human intelligence, the
risk of AI taking control over humanity, and
the implications for the workforce and the
spread of misinformation.
#AI #ArtificialIntelligence
#GeoffreyHinton #RomanesLecture
#FutureOfWork
7
8. GradSafe: Detecting Unsafe Prompts for
LLMs via Safety-Critical Gradient Analysis:
The article introduces GradSafe,
a method for detecting unsafe prompts in
Large Language Models (LLMs) by
analysing the gradients of safety-critical
parameters. GradSafe outperforms existing
methods by efficiently identifying unsafe
prompts without requiring extensive data
collection or training, demonstrating its
effectiveness with Llama-2 against the
Llama Guard system across different
evaluation datasets.
#AI #MachineLearning
#Cybersecurity #GradSafe #LLMs
8
9. Spreadsheets are all you need -
Understanding GPT2 and Transformers
with Spreadsheets: This article
discusses how the GPT-2 model and
Transformer architecture can be
understood through spreadsheets,
enabling even non-developers to explore AI
concepts directly with minimal abstraction.
#AI #GPT2 #Transformers #Excel
#MachineLearning
9
10. Generative Models: What do they know?
Do they know things? Let's find out!:
The article introduces INTRINSIC
LoRA (I-LoRA), a method that enhances
generative models like VQGAN and
StyleGAN to extract intrinsic scene
properties such as normals, depth, and
shading without additional layers,
showcasing their deep understanding of
scene intrinsics.
#GenerativeModels #INTRINSICLoRA
#SceneIntrinsics #AIResearch
#TechInnovation
10
11. Does Offering ChatGPT a Tip Cause it to
Generate Better Text? An Analysis
This article explores whether
offering incentives like tips or threats
within system prompts can enhance the
output quality of large language models
(LLMs), such as GPT-4, through a series of
inventive experiments. Despite varying
results, no definitive conclusion on the
effectiveness of these incentives could be
drawn
#ChatGPT #AIIncentives
#MachineLearning #DataScience
#ArtificialIntelligence
11
12. Demis Hassabis on Chatbots to AGI | Hard
Fork EP 71: Demis Hassabis
discusses Google's latest AI models, the
existential risks of AI, and the future of
artificial general intelligence (AGI), including
the temporary suspension of Gemini's
human image generation due to
controversial outputs.
#AI #GoogleAI
#ArtificialGeneralIntelligence
#GeminiGemma #FutureOfAI
12
13. Introduction to Matryoshka Embedding
Models: The article introduces
Matryoshka Embedding Models, which are
designed to produce useful embeddings of
variable sizes, allowing for more efficient
performance in downstream tasks without
a significant loss in effectiveness. These
models, inspired by Matryoshka dolls,
prioritise important information in smaller,
truncated embeddings for tasks like search
or classification.
#MatryoshkaEmbeddings #NLP #AI
#MachineLearning #DataEfficiency
13
14. I analysed 5M freelancing jobs to see what
jobs are being replaced by AI:
The article analyses 5M freelancing jobs to
identify the impact of AI on various job
categories, finding that writing, translation,
and customer service jobs saw significant
declines, whereas video production,
graphic design, and software development
jobs increased. It suggests that while AI
has replaced certain tasks, it has not yet
fully replaced creative and technical jobs.
#AIJobs #FreelancingTrends
#JobMarket #TechnologyImpact
#CareerAdvice
14
15. OWASP LLM AI Security and Governance
Checklist v1 (pdf): The OWASP
LLM AI Security and Governance Checklist
provides a comprehensive framework for
ensuring the security and responsible
governance of Large Language Models
(LLMs), addressing risks, legal and
regulatory considerations, and strategies
for deployment and evaluation.
#OWASP #AIsecurity #Governance
#LLM #Cybersecurity
15
16. Large Language Models: A Survey:
This is an excellent and highly
detailed primer on Large Language Models
(LLMs). This paper covers the significant
recent advances in natural language
processing, with key developments in
model families like GPT, LLaMA, and PaLM,
and includes ongoing research focusing on
building, augmenting, and evaluating these
models against various benchmarks. Also
in PDF format.
#LargeLanguageModels #ChatGPT
#LLaMA #PaLM #NLPResearch
16
17. OpenAI shocks the world yet again … Sora
first look: This video gives a
quick (5m) intro to OpenAI's SORA, a
groundbreaking AI that generates high-
definition, detailed videos from text
descriptions, capable of handling complex
scenes and occlusion effectively.
#OpenAI #SoraAI #VideoGeneration
#AIInnovation #CreativeAI
17
18. How I'd Learn AI (If I Had to Start Over):
This video provides a
comprehensive (if somewhat introductory)
guide for learning AI in 2024, covering
technical skills, theoretical fundamentals,
project ideas, specialised areas, AI safety,
regulations, and recommended resources
including courses, books, and newsletters
to achieve a well-rounded AI education.
This fantastic intro video also has a
companion Notion Site and a PDF. Well
worth a few minutes of your time.
#AI2024 #LearnAI #AIProjects
#AISafety #AIResources
18
19. Jeff Dean (Google): Exciting Trends in
Machine Learning: Jeff Dean,
Google's Chief Scientist, gives a (Google-
flavoured) talk on advancements in AI and
machine learning, highlighting the creation
of more capable, general-purpose systems
like the Gemini family of multimodal
models, and their applications in science,
engineering, and health, underscoring the
collaborative efforts at Google.
#AI #MachineLearning
#GoogleDeepMind #GeminiModels
#TechInnovation
19
20. GeneGPT: GeneGPT is a novel
approach designed to improve large
language models by utilizing NCBI Web
APIs for accurate biomedical information
retrieval, achieving state-of-the-art
performance on GeneTuring tasks. This
method not only enhances accuracy in
specialized knowledge areas but also
showcases the effectiveness of API
demonstrations over documentation for in-
context learning.
#GeneGPT #BiomedicalAI #NCBI
#APIIntegration #SOTAinGenomics
#HealthTech #Healthcare
20
21. Deep Learning Discovers Antibiotics:
Researchers have developed an innovative
approach using explainable deep learning to
identify new structural classes of antibiotics crucial
for combating antibiotic resistance. By employing
graph neural networks to analyse a vast array of
chemical compounds, they have successfully
discovered compounds effective against MRSA and
other resistant bacteria with low human toxicity.
This method surpasses traditional drug discovery
methods in efficiency, marking a significant
advancement in the ongoing fight against
antibiotic-resistant infections. More details in the
Nature paper here: Discovery of a structural class
of antibiotics with explainable deep learning.
#AntibioticResistance
#DeepLearningInMedicine #MRSA
#DrugDiscovery #AIinPharma #HealthTech
#Healthcare
21
22. GALA3D: Towards Text-to-3D Complex
Scene Generation via Layout-guided
Generative Gaussian Splatting:
This article introduces GALA3D, a tool for
creating realistic 3D scenes from text
descriptions using layout-guided
generative models and large language
models for layout descriptions, offering an
end-to-end framework for state-of-the-
art scene-level 3D content generation and
editing.
#GALA3D #3DModeling #GenerativeAI
#TextTo3D #TechInnovation
22
23. The Age of Average: This article
explores the homogenisation of culture and
creativity across various fields such as art,
interior design, architecture, automotive
design, personal appearance, and media. It
argues that despite the illusion of choice and
individuality, most creative domains have
converged towards a median, characterised by
widespread uniformity and a lack of
distinctiveness, leading to an era where
originality is rare. I have been referring to this
phenomenon as The Tyranny of the Banal.
#AgeOfAverage #CreativityCrisis
#CulturalHomogenisation
#UniformityInDesign
#LackOfOriginality
#TyrannyOfTheBanal
23
24. SORA Video To Video Is Literally Mind
Blowing - 12 HD Demos - Changes Industry
Forever For Real: The article
showcases a compilation of 12 Video-To-
Video #SORA demos by #OpenAI, highlighting
how this technology could revolutionise the
movie, animation, and social media industries
with its astonishing results. It delves into
Sora's technical aspects, including its use of
spatiotemporal latent patches, transformer-
based video diffusion models, and dataset
creation using high-precision video
captioning, without employing notably new
technology but rather emphasising the
importance of computational resources.
#OpenAI #VideoToVideo #SORA
#AIRevolution #TechInnovation
24
25. Enforced Amnesia as a Way to Mitigate the
Potential Risk of Silent Suffering in the
Conscious AI This article
discusses the concept of enforced amnesia
in AI as a preventive measure against the
potential suffering of conscious AIs by
interrupting their memory of past
experiences. This approach is proposed as
a moral and ethical consideration to
mitigate silent suffering in hypothetical
conscious AI systems without confirming
their consciousness.
#AIethics #ConsciousAI
#EnforcedAmnesia #DigitalEthics
#AIandMemory
25
26. OS-Copilot: Towards Generalist Computer
Agents with Self-Improvement
OS-Copilot introduces FRIDAY, a self-
improving agent framework for automating
a wide range of computer tasks,
demonstrating remarkable generalisation
and self-improvement abilities in operating
systems, web, and various applications,
significantly outperforming existing
methods on the GAIA benchmark.
#GeneralistAgents
#SelfImprovement
#ComputerAutomation #OSCopilot
#ArtificialIntelligence
26
27. Chain-of-Thought Reasoning Without
Prompting: The paper introduces
a novel method for eliciting chain-of-
thought reasoning from large language
models without the need for explicit
prompting. By altering the decoding
process, the study reveals that models can
inherently generate reasoning paths,
demonstrating a significant improvement
in reasoning capabilities and model
confidence over standard decoding
methods.
#AIResearch #LanguageModels
#ReasoningAI #InnovativeDecoding
#MachineLearning
27
28. Automated Unit Test Improvement using
Large Language Models at Meta
The paper discusses Meta's development of
TestGen-LLM, a tool leveraging Large
Language Models (LLMs) to enhance
existing software tests. It highlights how
TestGen-LLM improves code quality by
generating test cases that increase coverage
and pass reliability checks, evidenced by its
successful application in Instagram and
Facebook's development processes,
marking a significant step in automating and
improving software testing with AI.
#AIInSoftwareTesting #TestGenLLM
#CodeQuality #MetaInnovation
#AutomatedTesting
28
29. The AI bullshit singularity: The
article criticises the hype around AI and Large
Language Models (LLMs), arguing that
instead of leading to a technological
singularity of super-intelligence, we're more
likely to encounter a "bullshit singularity"
where the internet becomes flooded with
low-quality, AI-generated content, making it
difficult to discern truth. ED: There is more
than a little bit of irony with using GPT to
summarise an article criticising the rise of AI-
generated bullshit. Which is why, careful reader, I
make sure that I read what the AI-generates
and then editorialise as necessary.
#AICritique #TechSingularity #LLMs
#ContentQuality #DigitalFuture
#SanityCheck #AIBullshit
29
30. Sora is a data-driven physics engine:
OpenAI's Sora is not just a
creative tool but a sophisticated data-
driven physics engine capable of simulating
complex, realistic, or fantastical worlds
with detailed rendering and physics.
Although, there seems to be some debate
as to the degree to which Sora is actually a
"data-driven physics engine".
#OpenAISora #PhysicsSimulation
#DataDriven #Photorealism
#InnovativeTech
30
31. FCC Makes AI-Generated Voices in
Robocalls Illegal: The FCC has
declared AI-generated voice calls as illegal
under the Telephone Consumer Protection
Act, aiming to address the issue of artificial
robocalls.
#FCC #RobocallBan #AIVoices
#ConsumerProtection
#TelecomRegulations
31
32. SORA: Sora is OpenAI's AI model
capable of generating videos from text
prompts, creating realistic and imaginative
scenes that simulate real-world motion.
It's designed to assist in problem-solving
that requires real-world interaction and is
currently available to select visual artists,
designers, and filmmakers for feedback.
This is yet another mind-blowing piece of
generative AI functionality from OpenAI.
The "LLM Event Horizon" continues its
expansion at pace. First: text. Then: images.
Now: video. What will be the next category
consumed?
#SoraAI #OpenAI #TextToVideo
#AIInnovation #CreativeTech
32
33. Antagonistic AI: The paper
"Antagonistic AI" explores the concept of AI
systems designed to exhibit disagreeable
or challenging behaviours, arguing these
characteristics can sometimes offer
benefits like forcing users to confront
assumptions or build resilience. The
authors discuss the ethical considerations
and potential design strategies for such AI
systems.
#AntagonisticAI #AIethics
#InnovativeAI #UserExperience
#TechDebate
33
34. Is OpenAI the next challenger trying to take
on Google Search?: OpenAI is
reportedly developing a web search tool,
potentially integrated with Bing, to directly
challenge Google's search engine. This
initiative aligns with Microsoft CEO Satya
Nadella's strategy, as expressed last year,
to innovate in search technologies through
AI, notably with the Copilot AI tools in Bing.
The competitive landscape in search
engines is expanding, with Google's Bard/
Gemini, Copilot, and emerging players like
Perplexity joining the fray, indicating a
rapidly evolving market.
#OpenAI #Google #Bing
#SearchEngineWars #TechInnovation
34
35. Reduce AI Hallucinations with Retrieval
Augmented Generation: This
article discusses a new technique for
reducing AI-generated inaccuracies by
augmenting large language models (LLMs)
with proprietary data, which shows
promise in enhancing the models'
knowledge base.
#AI #LLMs #DataAugmentation
#MachineLearning
#TechnologyInnovation
35
36. AI Hallucinations : Fear Not — It’s A Solved
Problem — Here’s How (With Examples!):
The article discusses strategies to
mitigate AI hallucinations in generative models,
emphasising the necessity of integrating anti-
hallucination measures across the entire
Retrieval Augmented Generation (RAG) pipeline.
It argues that achieving near-perfect control
over hallucinations is crucial for reliability,
drawing parallels to business standards in
security and uptime. Techniques include
thorough testing, leveraging economies of scale
in SaaS platforms, and applying specific
technical solutions like query pre-processing
and dynamic context boundary walls in
prompts.
#AI #GenerativeModels #MachineLearning
#AIethics #TechInnovation
36
37. EmoSpeaker: One-shot Fine-grained
Emotion-Controlled Talking Face
Generation: EmoSpeaker
introduces a revolutionary technique for
generating emotional talking-head videos
from a single image, input audio, and
specified emotion, capable of adjusting
emotional intensity through fine-grained
control. This method surpasses existing
technologies in expression variation and
lip-sync accuracy.
#EmoSpeaker #TalkingHead
#EmotionalVideo #TechInnovation
#AIGeneratedContent
37
38. Sam Altman Seeks Trillions of Dollars to
Reshape Business of Chips and AI:
Sam Altman, CEO of OpenAI, is
seeking to raise trillions to expand global
semiconductor capabilities, aiming to
address the shortage of AI chips and
advance the development of artificial
general intelligence. A trillion here, a trillion
there. Pretty soon you're talking real
money.
#SamAltman #OpenAI
#SemiconductorIndustry
#ArtificialIntelligence
#TechInvestment
38
39. Machine Learning Research at Apple:
What Apple does with machine
intelligence in 2024 is anyone's guess.
Whereas the other Big Tech vendors tend
to release incrementally, Apple
(traditionally) likes to save up releases for
one big announcement each year, so we
will have to wait and see. Some
breadcrumbs are starting to emerge.
#MachineLearning #Apple
#TechInnovation #AnnualRelease
#BigTech
39
40. TikTok presents Boximator
TikTok introduces Boximator, a tool for
creating detailed and customisable motion
in image-to-video transformations using
box constraints and motion paths,
exemplified by a girl in red covering her
face with a skull, showcased through 10
unique examples.
#Boximator #TikTokInnovation
#ImageToVideo #CreativeTech
#MotionGeneration
40
41. Cory Doctorow: What Kind of Bubble is AI?:
Cory Doctorow's article in Locus
Magazine explores the nature of AI as a
bubble, comparing it to previous tech
bubbles. He discusses this bubble's
potential outcomes and remnants,
highlighting the distinction between
bubbles that leave valuable assets behind
and those that do not. Doctorow expresses
scepticism about AI's sustainable value and
business models, questioning what will
remain when the hype subsides.
#AIBubble #TechBubble
#CoryDoctorow #FutureOfAI
#TechScepticism
41
42. AI is the average of the Internet: WPP,
Don't become an AI North Korea:
This strongly worded opinion piece from
Punks and Pinstripes argues against WPP's
heavy investment in generative AI, likening
it to a decoy masking stagnation akin to
North Korea's strategy with nuclear
investment. It suggests that while AI can
handle operational tasks efficiently, it
stifles creativity in fields that thrive on
human ingenuity, urging companies to
balance AI use to avoid creative atrophy.
#AIInvestment #CreativeAtrophy
#BusinessStrategy
#InnovationVsTradition #WPP
42
43. AI assistance is leading to lower code
quality, claim researchers:
Research suggests that while popular and
enhancing productivity, AI coding
assistants like GitHub's Copilot may lead to
lower code quality, with issues like
increased code churn and higher amounts
of repeated code.
#AICoding #CodeQuality
#DeveloperTools #TechResearch
#SoftwareDevelopment
43
44. GPT4's system prompt was leaked:
This video breaks down the
leaked GPT4 system prompt. The
capabilities hinted at within the prompt are
very surprising. For example, the policy
statements for the use of DALL-E are
particularly interesting with respect to
emulating the style of artists.
#GPT4 #SystemPrompt #AI #LLM
#PromptEscape
44
45. Ancient Herculaneum scroll piece revealed
by AI: Artificial intelligence has
unlocked the contents of a papyrus scroll
from Herculaneum, revealing a Greek
philosopher's insights on pleasure,
previously hidden by the eruption of Mount
Vesuvius 2000 years ago. This
breakthrough, winning a $700,000 prize,
could lead to more ancient texts being
deciphered.
#AncientTexts #AI
#HerculaneumScrolls #Philosophy
#VesuviusChallenge
45
46. Beyond Self-Attention: How a Small
Language Model Predicts the Next Token:
This article explores how a small
transformer language model predicts the
next token, focusing on the role of
transformer blocks and feed-forward
networks beyond multi-head self-
attention. The author shares findings from
a six-month investigation, proposing that
each transformer block predicts the next
tokens based on learned associations with
classes of strings from the training data.
#AI #MachineLearning #Transformers
#LanguageModels #DeepLearning
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