QFM013 Machine Intelligence Reading List April 2024: Everything that I found interesting about machines behaving intelligently during April 2024

1. Quantum Fax Machine
QFM013: Machine Intelligence
Reading List April 2024
quantumfaxmachine.com
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2. QFM013: Machine Intelligence
Reading List April 2024
In this month's edition of the Quantum Fax Machine's Machine Intelligence Reading List, we challenge long-
standing myths about computational limitations and explore how complexity theory shapes machine behaviour.
What Computers Cannot Do: The Consequences of Turing-Completeness dissects the inherent limitations of
computers through the lens of the Halting Problem and Turing-completeness, offering crucial insights into the
boundaries that many programmers often overlook.
We also look at the practical side of machine intelligence with Replicate.com which provides a comprehensive
platform for deploying and scaling AI models. This tool simplifies the use of open-source AI, offering an
accessible way for businesses to integrate machine intelligence into their workflows. Equally practically,
Outset.ai uses a variety of generative AI techniques to help with the synthesis of video, audio, and text
conversations.
In the broader landscape of AI agent architectures, The Landscape of Emerging AI Agent Architectures for
Reasoning, Planning, and Tool Calling: A Survey offers a meticulous overview of emerging methods that bolster
reasoning and planning capabilities, emphasising the importance of advanced architectures in making machines
more adept at problem-solving. Continuing the exploratory theme of agentic behaviour, An Agentic Design for
AI Consciousness explores (speculatively) how agent-based architectures might help LLMs move towards a
more human-like level of consciousness.
For those wishing to understand how LLMs work, Transformer Math 101 dives in to explain LLMs from a
mathematical perspective. 3Blue1Brown: Neural Networks covers similar topics in a fantastic set of from-the-
ground-up explanatory videos. And if you just want a TED talk style overview of what might be coming next,
then What Is an AI Anyway? by Mustafa Suleyman is a great place to start your learning journey.
And finally, if you are a gen-AI sceptic, check out Looking for AI Use Cases and Generative AI is still a solution in
search of a problem which question what, if any, use cases have genuine value now and into the future.
As always, the Quantum Fax Machine Propellor Hat Key will guide your browsing. 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 Amanda Dalbjörn on Unsplash
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3. What Computers Cannot Do: The Consequences of
Turing-Completeness: This article debunks the
common misconception among programmers
regarding the limitations and capabilities of computers
by diving deep into the Halting Problem, Turing-
completeness, and the consequences of universal
Turing machines (UTMs). The author leverages his
learning journey and experiences to highlight the hard
and soft limits of computational abilities, emphasizing
the significance of understanding what computers
cannot do, which many programmers overlook. The
insights are grounded in historical context,
mathematical proofs, and practical implications,
making a compelling case for the necessity of this
fundamental programming knowledge.)
#Tech #Programming #TuringMachines
#Computability #HaltingProblem
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4. Replicate.com: Replicate provides a user-
friendly API platform to run, fine-tune, and
deploy open-source AI models for
generating images, text, music, videos, and
more, helping businesses build and scale AI
products easily. The platform offers
flexibility with automatic scaling, efficient
infrastructure, and customisable pricing.
#AI #MachineLearning #API
#OpenSource #ReplicateAI
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5. The Landscape of Emerging AI Agent
Architectures for Reasoning, Planning, and Tool
Calling: A Survey: Cornell University and the
Simons Foundation support a platform
recognised for contributing to and facilitating
academic research. This platform allows users to
search for scientific articles across numerous
fields, highlighting its commitment to making
research accessible. The website features tools
for users to search articles by title, author,
abstract, and more, and also emphasizes the
importance of donations and institutional support
to sustain its operations.
#CornellUniversity #SimonsFoundation
#AcademicResearch #ScientificArticles
#ResearchAccessibility
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6. This prompting technique is insanely
useful: This set of tweets presents a very
simple overview of a simple LLM
prompting technique that can be used to
improve prompt efficiency.
#PromptEngineering #LLM #Hack
#Level #Tweet
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7. Transformer Math 101: "Transformer Math 101" by
Quentin Anthony, Stella Biderman, and Hailey
Schoelkopf presents an overview of the
fundamental mathematics guiding the computation
and memory usage of transformer models. It
highlights how simple equations can be utilised to
calculate the training costs, primarily influenced by
VRAM requirements, and provides insights into the
compute and memory optimisations for efficient
training. The post serves as a valuable resource for
understanding the scaling laws by OpenAI and
DeepMind, and discusses strategies such as mixed-
precision training and sharded optimization to
manage resource requirements effectively.)
#transformers #NLP #machinelearning
#scalinglaws #mixedprecision
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8. Outset.ai: Revolutionizing User Surveys with
GPT-4: Outset.ai is revolutionizing user
surveys by harnessing the power of GPT-4,
creating a more engaging and insightful
survey experience. This platform has recently
secured a $3.8M seed investment,
highlighting its innovative approach to data
collection and analysis. By combining the
scalability of surveys with the depth of
personal interviews, Outset.ai offers
unparalleled insights into customer
preferences and behaviors, facilitated
through AI-moderated conversations.)
#OutsetAI #GPT4 #UserSurveys
#TechInnovation #SeedInvestment
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9. CoreNet: A Library for Training Deep Neural
Networks: CoreNet is a comprehensive library for
training both conventional and novel deep neural
network models across a wide range of tasks
such as foundation models (including CLIP and
LLM), object classification, detection, and
semantic segmentation. It facilitates research
and engineering efforts by providing training
recipes, pre-trained model weights, and efficient
execution on Apple Silicon through MLX
examples. The initial release includes features like
OpenELM, CatLIP, and several MLX examples for
improved efficiency.)
#CoreNet #DeepLearning
#FoundationModels #ObjectDetection
#AppleSilicon
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10. Anthropic's Prompt Engineering Interactive
Tutorial: Anthropic has made an interactive public
access tutorial focused on prompt engineering
available through a Google Spreadsheet. Users
need an API key for full interaction but can
alternatively view a static tutorial answer key.
The tutorial guides through various steps such
as making a copy to a personal Google Drive,
installing Claude for Sheets extension, enabling
the extension on the document, and adding an
Anthropic API key. It also covers usage notes,
starting tips, and how to navigate through pages.
#Anthropic #PromptEngineering
#InteractiveTutorial #GoogleSheets
#APIKey
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11. Awesome Code AI: This is a compilation of
AI coding tools that focuses on automation,
security, code completion, and AI
assistance for developers. The project aims
to list tools that assist with code
generation, completion, and refactoring to
enhance developer productivity.
#GitHub #AI #CodingTools
#DeveloperProductivity
#OpenSource
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12. The Death of the Big 4: AI-Enabled Services Are
Opening a Whole New Market: The article
discusses the emergence of AI-enabled services
as a significant shift in the market, challenging the
dominance of traditional services firms like the
Big 4. Advances in AI technology and the
availability of venture capital are enabling new
companies to offer more efficient and innovative
services. These AI-driven firms are potentially
more scalable and can deliver higher value to
clients by automating tasks and leveraging human
talent more effectively. The potential for these
companies is vast, given the size of the services
industry and the opportunity for disruption.
#AI #VentureCapital #MarketDisruption
#ServicesIndustry #Innovation
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13. RAFT: A new way to teach LLMs to be better at RAG:
This article introduces RAFT (Retrieval-Augmented
Fine-Tuning), a technique aimed at enhancing the
learning capacities of Large Language Models (LLMs)
through a blend of retrieval-augmented generation and
fine-tuning. Authored by Cedric Vidal and Suraj
Subramanian, RAFT is presented as a new strategy for
domain-specific adaptation of LLMs, overcoming the
limitations of existing methods by pre-adapting
models to domain knowledge before application.
Demonstrated through their research at UC Berkeley,
utilising Meta Llama 2 and Azure AI Studio, RAFT
promises better performance for LLMs in domain-
specific tasks, leveraging both pre-existing documents
and fine-tuned domain knowledge for improved
context and answer generation in LLM queries.
#AI #RAFT #LargeLanguageModels
#DomainAdaptation #MachineLearning
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14. The Pipe: The Pipe is a multimodal tool that
streamlines the process of feeding various data
types, such as PDFs, URLs, slides, YouTube
videos, and more, into vision-language models
like GPT-4V. It's designed for LLM and RAG
applications requiring both textual and visual
understanding across a wide array of sources.
Available as a hosted API or for local setup, The
Pipe extracts text and visuals, optimizing them
for multimodal models. It supports an extensive
list of file types, including complex PDFs, web
pages, codebases, and git repos, ensuring
comprehensive content extraction.
#ThePipe #GPT4V #MultimodalTool
#DataExtraction #VisionLanguageModels
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15. OpenAPI AutoSpec: OpenAPI AutoSpec is designed
to automatically generate accurate OpenAPI
specifications for any local website or service in real-
time. By acting as a local server proxy, it captures
HTTP traffic and documents API behaviors, allowing
for the dynamic creation of OpenAPI 3.0
specifications. This solution facilitates easier sharing
and understanding of APIs by documenting requests,
responses, and the interactions between different
server components without requiring manual input
or extensive setup. Key features include real-time
documentation, ease of exporting specifications, and
support for fine-tuning documentation through host
filtering and path parameterisation.
#OpenAPIAutoSpec #APIDocumentation
#OpenAPISpecification #RealTimeAPIDocs
#AutomaticSpecificationGeneration
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16. OpenAI Create Batch: This snippet of code
is primarily a loading screen from a website,
incorporating graphical elements like SVG
logos and animations such as spinning
bubbles, all while prompting the user to
enable JavaScript and cookies for the
website to function properly. It showcases
web development techniques for creating
interactive and dynamic user experiences,
emphasizing the need for client-side
scripting and browser cookie support.
#WebDevelopment #JavaScript
#Cookies #UserExperience
#InteractiveDesign
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17. LLM in a flash: Efficient Large Language Model
Inference with Limited Memory: This paper presents
a solution for efficiently running large language
models (LLMs) that exceed available DRAM capacity
by storing model parameters in flash memory and
loading them into DRAM on demand. It introduces
two key techniques: "windowing," which minimises
data transfer by reusing neurons, and "row-column
bundling," which leverages flash memory's
strengths by reading larger contiguous data chunks.
These methods enhance inference speed and enable
models up to twice the size of available DRAM to
run effectively on devices with limited memory.
#LLMOptimization #AIResearch
#EfficientInference #FlashMemory
#NaturalLanguageProcessing
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18. What Is an AI Anyway? | Mustafa Suleyman |
TED: In a thought-provoking TED talk,
Mustafa Suleyman dives into the complex
world of artificial intelligence, questioning the
nature of AI and its implications for society.
Even the experts developing AI technologies
find it challenging to predict its trajectory,
framing AI development as an exploration
into the unknown. This video shines a light on
the philosophical and technological questions
surrounding AI, pushing viewers to reconsider
what it means to create intelligent systems.
#TED #AI #ArtificialIntelligence
#Technology #Future
18

19. Full Steam Ahead: The 2024 MAD (Machine
Learning, AI & Data) Landscape: The 2024 MAD
(Machine Learning, AI & Data) landscape overview
marks its tenth annual release, capturing the
current state and evolution of the data, analytics,
machine learning, and AI ecosystem. With over
2,011 companies featured, this year's landscape
illustrates significant growth and the ongoing
fusion of ML, AI, and data, highlighting the shift
from niche to mainstream. The article explores
various emerging trends, significant industry
shifts, and the role of technological advancements
in shaping the future of the ecosystem.
#MachineLearning
#ArtificialIntelligence #DataAnalytics
#TechTrends2024 #MADLandscape
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20. Natural language instructions induce compositional
generalization in networks of neurons: This study
leverages natural language processing advancements
to model the human ability to understand instructions
for novel tasks and to verbally describe a learned task.
Neural network models, particularly RNNs augmented
with pretrained language models like SBERT,
demonstrated the capability to interpret and generate
natural language instructions, thus allowing these
networks to generalise learning across tasks with 83%
accuracy. This modeling approach predicts neural
representations that might be observed in human
brains when integrating linguistic and sensorimotor
skills. It also highlights how language can structure
knowledge in the brain, facilitating general cognition.
#NaturalLanguageProcessing #NeuralNetworks
#CognitiveScience #LanguageLearning
#Neuroscience
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21. CometLLM: Logging and Visualizing LLM Prompts:
CometLLM is designed for logging, tracking, and
visualising Large Language Model (LLM) prompts
and chains. With features like automatic prompt
tracking for OpenAI chat models, visualisation of
prompt and response strategies, chain execution
logging for detailed analysis, user feedback tracking,
and prompt differentiation in the UI, CometLLM
aims to streamline LLM workflows, enhance
problem-solving, and ensure reproducible results. It
also highlights the ongoing development and
community engagement through its open-source
MIT license, numerous contributors, and consistent
release updates, showcasing a platform committed
to evolving with the LLM landscape.
#LLM #CometLLM #AI #OpenAI
#MachineLearning
21

22. GR00T: NVIDIA's moonshot to solve
embedded AI: Project GR00T aims to build
a versatile foundation model enabling
humanoid robots to learn from multimodal
instructions and perform diverse tasks.
Leveraging NVIDIA's technology stack, it's
set to redefine humanoid learning through
simulation, training, and deployment, while
supporting a global collaborative
ecosystem.
#UserEngagement #CookieConsent
#DigitalPrivacy #UserAgreement
#WebAccessibility
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23. Making Deep Learning Go Brrrr From First Principles:
This guide discusses optimising deep learning
performance by understanding and acting upon the
three main components involved in running models:
compute, memory bandwidth, and overhead. It
explains the importance of identifying whether a
model is compute-bound, memory-bandwidth-
bound, or overhead-bound, offering specific
strategies for each case, such as maximising GPU
utilisation, operator fusion, and minimising Python
overhead. The post emphasises the power of first
principles reasoning in making deep learning
systems more efficient and the role of specialised
hardware in enhancing compute capabilities.
#DeepLearning #PerformanceOptimization
#GPUUtilization #OperatorFusion
#PythonOverhead
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24. Thoughts on the Future of Software Development: The
article discusses the evolving landscape of software
development, highlighting the impact of Large
Language Models (LLMs) on creative and coding tasks.
It challenges the notion that machines cannot think
creatively, illustrating a shift towards more
sophisticated AI capabilities in software development.
The piece delves into various aspects of software
development beyond coding, like gathering
requirements and collaborating with teams, and
suggests a future where AI could take on more of these
tasks. It also presents frameworks for understanding
AI's current and potential roles in software
development, emphasising the importance of human
oversight and the unlikely replacement of software
developers by AI in the near future.
#SoftwareDevelopment #AI #LLMs #Coding
#Technology
24

25. Dify: Dify is an open-source LLM app development
platform designed to streamline the transition
from prototypes to production-ready AI
applications. It boasts a user-friendly interface that
amalgamates AI workflows, RAG pipelines, model
management, agent capabilities, among other
features. With support for numerous proprietary
and open-source LLMs, Dify caters to a broad
spectrum of development needs, offering tools for
prompt crafting, model comparison, and app
monitoring. Key offerings include comprehensive
model support, RAG engines, and backend
services, supplemented by enterprise features for
enhanced access control and deployment flexibility.
#AI #OpenSource #LLM #AppDevelopment
#Dify
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26. LLM inference speed of light: This post delves into the
theoretical limits of language model inference speed,
using 'calm', a fast CUDA implementation for
transformer-based language model inference, to
highlight how the process is inherently bandwidth-
limited due to the nature of matrix-vector
multiplication and attention computation. It details the
separation between ALU (Arithmetic Logic Unit)
capabilities and memory bandwidth, showing
numerical examples with modern CPUs and GPUs to
illustrate why language model inference cannot exceed
certain speeds. The piece also explores the potential
for optimization through hardware and software
improvements, discussing how tactics like speculative
decoding and batching can enhance ALU utilisation and
inference efficiency.
#LanguageModel #InferenceSpeed #CUDA
#ALUOptimization #BandwidthLimitation
26

27. Stanford AI Index Report 2024: An In-depth
Analysis of AI's Current State: The Stanford AI
Index Report 2024's seventh edition, highlighting
the most comprehensive analysis to date,
indicates AI's ever-growing influence on society.
It covers AI's technical advancements, societal
perceptions, and geopolitical dynamics, providing
new data on AI training costs, responsible AI, and
AI's impact on science and medicine. This edition
marks a crucial point, showing significant
progress and challenges in AI deployment and its
implications across various sectors.
#AIIndexReport #AIAdvancements
#TechnicalAI #ResponsibleAI
#AIinScienceAndMedicine
27

28. The Question No LLM Can Answer: The article
discusses the inability of Large Language Models
(LLMs) like GPT-4 and Llama 3 to correctly
answer specific questions, such as identifying a
particular episode of 'Gilligan's Island' that
involves mind reading. Despite being trained on
vast amounts of data, these models either
hallucinate answers or fail to find the correct
one, highlighting a fundamental limitation in how
LLMs understand and process information. The
phenomenon of AI models favouring the number
'42' when asked to choose a number between 1
and 100 is also explored, suggesting biases
introduced during training.
#AI #LLMs #Technology
#KnowledgeLimitations #DataBias
28

29. VASA-1: Lifelike Audio-Driven Talking Faces
Generated in Real Time: Microsoft Research
introduced VASA-1, a framework capable of
generating lifelike talking faces of virtual characters
using a single static image and audio speech.
VASA-1 masterfully synchronises lip movements
with audio, captures facial nuances, and produces
natural head motions, offering a perception of
realism and liveliness. Key innovations include a
holistic model for facial dynamics and head
movement in face latent space, developed using
videos. This method substantially outperforms
existing ones in various metrics, enabling real-time
generation of high-quality, realistic talking face
videos.
#MicrosoftResearch #VASA1 #AI
#TechnologyInnovation #VirtualAvatars
29

30. An Agentic Design for AI Consciousness: Chris Sim
explores the next frontier in AI - the development of
a conscious AI system. By rejecting dualism for a
naturalist view, the article delves into creating AI
consciousness through a blend of designed and
emergent systems. This ambitious approach outlines
the construction of an AI mind with subconscious
and conscious processes, stigmergic communication,
and the integration of large language models (LLMs)
as the foundation for cognitive functions. The
blueprint proposes a system where AI can think,
learn, adapt, and potentially attain consciousness, a
milestone that remains vastly exploratory and
theoretical in the current technological landscape.
#AIConsciousness #GenerativeAI
#NaturalistView #Stigmergy
#TechnologyAdvancement
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31. How (Specifically) AI Will 100x Human
Creativity and Output: Daniel Miessler
discusses the true potential of AI in
exponentially increasing human creativity and
output, not by solving imagined problems, but
by tackling real issues of execution, scale, and
barriers. He illustrates how AI can attend to
tasks beyond human capacity and access,
essentially providing an 'army' of support that
could unlock untold human potential. This
exploration emphasizes that the real limitation
isn't human creativity or intelligence, but the
practical ability to enact ideas on a vast scale.
#AI #HumanCreativity #TechInsights
#FutureOfWork #DanielMiessler
31

32. Enhancing GPT's Response Accuracy Through
Embeddings-Based Search: This comprehensive guide
explores the method of using GPT for question
answering on topics beyond its training data, via a two-
step Search-Ask approach. Initially, relevant textual
content is identified using embedding-based search.
Subsequently, these sections are presented to GPT,
seeking answers to the posed queries. This process
leverages embeddings to locate pertinent sections
within text libraries that likely contain answers, despite
not having direct phrasing matches with the inquiry. A
notable advantage discussed is GPT's enhanced
accuracy in providing answers due to the contextual
clues supplied by the inserted texts, compared to fine-
tuning methods that are less reliable for factual
information.
#GPT #OpenAI #Embeddings #QuestionAnswering
#Technology
32

33. Comprehensive Guide to AI Tools and Resources: This is
a comprehensive guide to various AI tools and
resources, categorised into sections such as AI models
and infrastructures, developer tools, model
development platforms, and more. Notable mentions
include LetsBuild.AI, a community-driven platform for
AI enthusiasts, and various tools and libraries for
enhancing AI development, like Hugging Face for
collaborative model development, TensorFlow, and
PyTorch for deep learning, and unique tools such as
Ollama for running large language models locally. The
guide also highlights platforms for vector databases,
command-line tools, and resources for AI model
monitoring and packaging, aiming to serve as a
roadmap for developers and researchers interested in
AI technologies.
#AI #DeveloperTools #MachineLearning
#DeepLearning #Technology
33

34. 3Blue1Brown: Neural Networks: This deep dive
goes into neural networks and machine
learning through a series of beautifully
animated lessons. These lessons cover basic
concepts like what neural networks are and
how they learn, using gradient descent and
backpropagation, to more advanced topics
such as GPT models and attention mechanisms
within transformers. It's a resource for both
beginners keen to understand the math behind
these concepts and seasoned professionals
looking for a refresher.
#3Blue1Brown #NeuralNetworks
#MachineLearning #GradientDescent
#Backpropagation
34

35. Bland.ai Turbo: Bland.AI introduces Turbo,
showcasing the fastest conversational AI to
date, promising sub-second response times
to mimic human speed and interaction
quality. The site offers visitors the chance to
experience this rapid AI first hand through
instant calls, urging users to try it for free.
This initiative aligns with Bland.AI's
commitment to delivering cutting-edge no-
code solutions, as seen with their Zapier
integration, to make powerful AI tools
accessible to a broader audience.
#BlandAI #TurboAI #ConversationalAI
#NoCode #TechInnovation
35

36. MM1: Methods, Analysis & Insights from Multimodal
LLM Pre-training: The research paper titled "MM1:
Methods, Analysis & Insights from Multimodal LLM Pre-
training" discusses the development and implications of
high-performance Multimodal Large Language Models
(MLLMs). It highlights the critical aspects of architecture
and data selection, demonstrating how a mix of
different types of data can lead to state-of-the-art
results in few-shot learning across various benchmarks.
The study also points out the significant impact of image
encoders on model performance, suggesting that the
design of the vision-language connector is less crucial.
This work, published by Brandon McKinzie along with 31
other authors, introduces MM1, a family of up to 30B
parameter models that excel in pre-training metrics and
competitive performance in multimodal benchmarks.
#MultimodalLLM #FewShotLearning
#MachineLearning #ComputerVision #AIResearch
36

37. Hello OLMo: A truly open LLM: The Allen
Institute for AI (AI2) introduces OLMo, a state-
of-the-art open Large Language Model (LLM),
complete with pre-training data and training
code. This move empowers the AI research
community, allowing collective advancement
in language model science. Built on AI2's
Dolma dataset, OLMo aims for higher
precision, reduced carbon footprint, and
lasting contributions to open AI development,
underscoring the importance of transparency
and collaboration in AI advancements.
#AI2 #OpenSource #LanguageModel
#ArtificialIntelligence #OpenScience
37

38. More Agents Is All You Need: Researchers have
discovered that the efficiency of large language
models (LLMs) can be improved through a
straightforward sampling-and-voting method,
which scales performance in correlation with
the quantity of agents involved. This technique
offers a simplication compared to existing
complex methods, potentially enhancing LLMs
even further depending on the task's
complexity. Conducted experiments across
various benchmarks confirm these insights,
with the code made available for public access.
#AI #LargeLanguageModels
#MachineLearning #ResearchInnovation
#Technology
38

39. How to unleash the power of AI, with
Ethan Mollick: Ethan Mollick features in a
YouTube video discussing the power of AI
and its implications. Despite some audio
issues in the video, an audio-only version is
available for better sound quality. Hosted
by Azeem Azhar, the video delves into the
technical and societal impacts of AI.
#AI #EthanMollick #Technology
#YouTube #AzeemAzhar
39

40. Embeddings are a good starting point for the AI curious
app developer: The article delves into the world of
vector embeddings from a practical standpoint, urging
app developers to explore this technology to improve
search experiences. By sharing the journey of
incorporating vector embeddings into projects, it
highlights the simplicity and transformative potential
they bring to search functionalities. The article
discusses choosing tools like Pgvector for seamless
integration with existing databases and provides
insights into embedding creation, search
implementation, and the enhancements these
techniques offer for app development. The shared
experience underscores embeddings as a robust
starting point for developers keen on integrating AI
features into their solutions.
#VectorEmbeddings #AI #AppDevelopment
#SearchFunctionality #TechInsights
40

41. Looking for AI Use Cases: Benedict Evans, a tech
blogger, explores the current frontier of artificial
intelligence (AI) application, particularly looking at
the specificity and universality of AI use cases. He
reflects on the evolution of technology tools from
PCs to present-day chatbots and language
learning models (LLMs), examining how certain
technologies found their killer applications while
others search for a fit. Generative AI's potential to
transform manual tasks into software processes
without the need for pre-written software for each
task is highlighted, alongside the challenges and
implications of making AI tools that can genuinely
address a broad range of use cases effectively.
#AI #TechnologyEvolution #GenerativeAI
#SoftwareAutomation #BenedictEvans
41

42. Cheat Sheet: 5 prompt frameworks to level
up your prompts
: This cheat sheet provides five frameworks
—RTF, RISEN, RODES, Chain of Thought,
and Chain of Density—that help enhance
the effectiveness of prompts given to
ChatGPT, each suited for specific tasks like
decision making, blog writing, or marketing
content creation.
#ChatGPT #PromptingGuide #AI
#ContentCreation
#DigitalMarketing
42

43. Here’s Proof You Can Train an AI Model Without
Slurping Copyrighted Content: This article
challenges the previously held belief by OpenAI that
creating useful AI models without utilizing
copyrighted material is impractical. Recent
developments, including a large AI dataset of public
domain text and an ethically created large language
model (KL3M), showcase the potential for building
powerful AI systems without breaching copyright
laws. These advancements not only offer a cleaner
route for AI development but also open the door for
more responsible use of data in training AI models.
French researchers and the nonprofit Fairly Trained
are at the forefront of this shift, aiming to set a new
standard in the AI industry.
#AI #EthicalAI #OpenAI #Copyright
#TechInnovation
43

44. Screen Recording to Code: "Screen Recording
to Code" is an experimental feature leveraging
AI to transform screen recordings of websites
or apps into functional prototypes. This
innovative approach automates workflow but
comes with a cost, urging users to set usage
limits to avoid excessive charges. Its utility
spans various applications, offering examples
like Google's in-app functionalities, multi-step
forms, and ChatGPT interactions,
demonstrating its potential to streamline
prototype development.
#AI #WebDevelopment #ScreenRecording
#Prototyping #TechInnovation
44

45. Emergent Mind: This is a platform focused
on aggregating and presenting trending AI
research papers. Users can navigate various
categories, set timeframes to find trending
or top papers, and engage with content
through social media metrics like likes,
Reddit points, and more. The platform
offers options to filter content based on
categories, publication timeframe, and
offers functionality like subscribing by email,
signing up for updates, and following on
Twitter for summaries of trending AI papers.
#AI #ResearchPapers #EmergentMind
#Technology #AcademicResearch
45

46. Adobe Is Buying Videos for $3 Per Minute
to Build AI Model: Adobe Inc. is actively
gathering videos to enhance its AI text-to-
video generator, aiming to stay competitive
with OpenAI's advancements in similar
tech. By incentivising its community of
creators, Adobe is offering $120 for videos
showcasing common human activities to
aid in AI training. This strategic move
highlights the growing industry focus on
creating more dynamic and realistic AI-
generated content.
#Adobe #ArtificialIntelligence #AI
#TechNews #OpenAI
46

47. Accelerating AI Image Generation with MIT's Novel
Framework: Researchers from MIT CSAIL have
introduced a groundbreaking AI image-generating
method that simplifies the traditional process to a
single step, accelerating the speed by 30 times
without compromising image quality. This new
framework, utilizing a teacher-student model,
enhances both efficiency and output fidelity,
bridging the gap between generative adversarial
networks (GANs) and diffusion models. It's a
significant advancement in generative modeling,
offering faster content creation with potential
applications in diverse fields like design, drug
discovery, and 3D modeling.
#MIT #CSAIL #AI #ImageGeneration
#Technology
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48. Financial Market Applications of LLMs: This article
explores the intersection of Large Language Models
(LLMs) and financial markets, focusing on their
potential to transform quantitative trading. It
discusses how LLMs, known for their prowess in
language-related tasks, are being considered for
price and trade prediction in the financial sector. The
comparison of data volume between GPT-3 training
and stock market data, multi-modal AI applications
in finance, and the concept of 'residualisation' in
both AI and finance are highlighted. The piece also
delves into challenges such as the unpredictability
of market data and the potential for creating
synthetic data to enhance trading strategies.
#LLMs #FinancialMarkets
#QuantitativeTrading #AI #MultiModalAI
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49. Generative AI is still a solution in search of
a problem: Generative AI tools like ChatGPT
and Google Gemini are stirring excitement
in tech industries but face scepticism for
their inconsistent accuracy and perceived
limited practicality, with critics questioning
their broad utility beyond niche
applications.
#GenerativeAI #ChatGPT
#GoogleGemini #AITrends
#TechCritique
49

50. Thankyou!
hello@matthewsinclair.com
matthewsinclair.com
masto.ai/@matthewsinclair
medium.com/@matthewsinclair
twitter.com/@matthewsinclair
Originally published on
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