ARTIFICIAL INTELLIGENCE
WHAT IS AI ?
Artificial intelligence examples
At the simplest level, machine learning
uses algorithms trained
on data sets to create machine learning models that allow
computer systems to perform tasks like making song recommendations, identifying
the fastest way to travel to a destination, or translating text from one
language to another. Some of the most common examples of AI in use today
include:
· Chat GPT: Uses large language models (LLMs) to generate text in
response to questions or comments posed to it.
·
Google
Translate: Uses deep learning algorithms to
translate text from one language to another.
·
Netflix: Uses machine learning algorithms to create
personalized recommendation engines for users based on their previous viewing
history.
·
Tesla: Uses computer vision to power self-driving features
on their cars.
The
4 Types of AI
1. Reactive machines
Reactive machines are the most basic type
of artificial intelligence. Machines built in this way don’t possess any
knowledge of previous events but instead only “react” to what is before them in
a given moment. As a result, they can only perform certain advanced tasks
within a very narrow scope, such as playing chess, and are incapable of
performing tasks outside of their limited context.
2. Limited memory machines
Machines with limited memory possess a
limited understanding of past events. They can interact more with the world
around them than reactive machines can. For example, self-driving cars use a
form of limited memory to make turns, observe approaching vehicles, and adjust
their speed. However, machines with only limited memory cannot form a complete
understanding of the world because their recall of past events is limited and
only used in a narrow band of time.
3. Theory of mind machines
Machines that possess a “theory of mind”
represent an early form of artificial general intelligence. In addition to
being able to create representations of the world, machines of this type would
also have an understanding of other entities that exist within the world. As of
this moment, this reality has still not materialized.
4. Self-aware machines
Machines with self-awareness are the
theoretically most advanced type of AI and would possess an understanding of
the world, others, and itself. This is what most people mean when they talk
about achieving AGI. Currently, this is a far-off reality.
BENEFITS AND DANGERS
|
Potential Benefits |
Potential Dangers |
|
Greater accuracy for certain repeatable tasks, such as assembling
vehicles or computers. |
Job loss due to increased automation. |
|
Decreased operational costs due to greater efficiency of machines. |
Potential for bias or discrimination as a result of the data set on
which the AI is trained. |
|
Increased personalization within digital services and products. |
Possible cybersecurity concerns. |
|
Improved decision-making in certain situations. |
Lack of transparency over how decisions are arrived at, resulting in
less than optimal solutions. |
|
Ability to quickly generate new content, such as text or images. |
Potential to create misinformation, as well as inadvertently violate
laws and regulations. |
Sources of AI
2. Tech
Companies: Large technology companies such as Google, Microsoft, Facebook,
Amazon, and IBM have significant investments in AI research and development.
They often have dedicated AI research labs and teams.
3. Startups: Numerous AI startups are founded each year, focusing on various
applications of AI, from healthcare to finance to entertainment.
4. Open
Source Community: There's a vast amount of AI software and tools available as
open source, developed and maintained by the global community of developers and
researchers.
5. Government Agencies: Some governments fund AI research directly or
indirectly through grants and contracts.
6. Online
Platforms: Platforms like GitHub, Kaggle, and Stack Overflow host
repositories of AI-related projects, datasets, and discussions, serving as
valuable resources for AI enthusiasts and professionals.
7. Academic Journals and Conferences: AI research is published in academic
journals and presented at conferences, where researchers share their findings
and advancements in the field.
8. Collaborative Efforts: AI advancements often come through collaborations
between different entities, including academia, industry, and government.
LLAMA3
Llama 3's
unrivaled performance is thanks to major improvements in its pretraining
process and architecture. The model was trained on a massive dataset of over 15
trillion tokens from publicly available sources, an astounding 7 times more
data than Llama 2. This includes 4 times more code data to boost Llama 3's
coding capabilities, as well as significant coverage of 30+ languages to lay
the foundation for future multilingual versions. Extensive filtering was used
to curate this data, ensuring Llama 3 learned from only the highest quality
sources.
But Llama 3's enhancements go beyond just more data. Cutting-edge optimizations to the model's architecture and training process have substantially improved its reasoning abilities, code generation, instruction following, and response diversity. An improved tokenizer makes Llama 3 up to 15% more token efficient than its predecessor. Grouped query attention allows the 8B model to maintain inference parity with the previous 7B model
Creative Generation: Llama 3 can generate highly coherent and creative text in the form of stories, scripts, musical pieces, poems, and more.
Coding and Reasoning: Thanks to its enhanced code training data, Llama 3 boasts incredibly strong coding and logical reasoning skills for tackling intricate problems.
Question Answering: By connecting information across its broad knowledge base, Llama 3 can provide deeply knowledgeable answers to questions on diverse topics.
Summarization: Llama 3 is adept at producing concise yet comprehensive summaries of long articles and factual content.
Instruction Following: One of Llama 3's most impressive feats is its ability to accurately follow complex multi-step instructions for open-ended tasks.
BLOOM
What sets BLOOM apart is its
open-access nature – the model, source code, and training data are all freely
available under open licenses, in contrast to most other large language models
developed by tech companies. This openness invites ongoing examination,
utilization, and enhancement of the model by the broader AI community.
BLOOM boasts impressive
multilingual capabilities, having been trained on a vast 1.6TB dataset (the
ROOTS corpus) spanning 46 natural languages and 13 programming languages, with
over 30% of the data being English. For many languages like Spanish and Arabic,
BLOOM is the first model of its size.
The model was trained over 3.5
months on the Jean Zay supercomputer in France using 384 NVIDIA A100 GPUs, made
possible by a compute grant from the French government – equating to over 5
million hours of compute. Based on the GPT architecture with modifications,
BLOOM achieves competitive performance on benchmarks.
Key Strengths of BLOOM:
Open-Access: BLOOM's model, code and training data are freely available, democratizing access to powerful language models and enabling open research.
Multilingual Proficiency: Trained on data spanning 46 natural languages and 13 programming languages, BLOOM has extensive multilingual capabilities.
Versatile Language Skills: From text generation to question answering, summarization, translation, and code generation, BLOOM excels at a variety of language tasks.
Responsible AI Development: BLOOM was developed with a focus on responsible AI practices and is released under a license prohibiting malicious use cases.
Easy Deployment: Developers can access BLOOM through the Hugging Face Transformers library and deploy it using Accelerate.
FALCON2
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Falcon 2 is the latest generation of open-source large language models from the Technology Innovation Institute (TII) in Abu Dhabi, building upon the success of their earlier Falcon 7B, 40B, and 180B models released in 2023. The Falcon 2 series currently includes:
Falcon 2 11B: An 11 billion parameter causal decoder-only model that outperforms Meta's LLaMA 3 8B and performs on par with Google's Gemma 7B model on standard benchmarks, as verified by the Hugging Face leaderboard.
Falcon 2 11B VLM: A groundbreaking multimodal version of Falcon 2 11B with vision-to-language capabilities, making it one of the only open-source models to offer this functionality.
The Falcon 2 models were
trained on over 5 trillion tokens from the enhanced RefinedWeb dataset, which
includes a diverse mix of high-quality web data, books, technical writing,
code, and conversations. Extensive filtering and deduplication techniques were
used to extract the best data. While still primarily English-focused, a portion
of the training data covers other languages like German, Spanish, French, and
Italian, laying the groundwork for future multilingual models.
Falcon 2 utilizes
an optimized decoder-only transformer architecture that enables strong
performance at a smaller scale compared to other open models. TII plans to
further boost efficiency using techniques like mixture-of-experts in upcoming
releases.
In terms of raw capabilities, Falcon 2 11B excels at a wide range of natural language tasks, including:
Text generation of coherent long-form content like stories and articles
Knowledgeable question answering by connecting information on diverse topic
High-quality summarization of long articles or factual content
Accurate instruction following when fine-tuned
Solid performance on coding and reasoning benchmarks
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