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AIdeations 2023-2024: A Year in Review and a Look Ahead
From GPT-4's Rise to Legal Battles: Navigating the Evolving AI Landscape
Brent Moreno
December 28, 2023
It feels good to be back after taking the first full week off since I started writing Aideations 1 year ago today. Honestly I hated the time off away from writing so I doubt that happens again but then I also got sick. I guess that’s what I get for climbing out of my hole.
Anyways, here’s what’s in store today!
Generative AI's Meteoric Rise: 2023 witnessed the unprecedented growth of large language models like GPT-4, impacting diverse fields and sparking significant debates.
New York Times vs. OpenAI: A pivotal lawsuit challenging the boundaries of AI and copyright law, questioning the originality of AI-generated content.
Samsung's AI-Powered Fridge: The unveiling of a technologically advanced fridge, blending AI with health and kitchen management.
2024 AI and Data Predictions: Key trends reshaping the enterprise landscape, from evolving database technologies to AI's role in decision-making.
SOLAR 10.7B Research Highlight: A novel approach in large language model scaling, offering enhanced performance and public accessibility.
📰 News From The Front Lines
📖 Tutorial Of The Day
🔬 Research Of The Day
📼 Video Of The Day
🛠️ 6 Fresh AI Tools
🤌 Prompt Of The Day
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Celebrating Our First Anniversary with a Look Back at 2023 and Forward to 2024
As AIdeations celebrates its first anniversary, it's an opportune moment to reflect on the tumultuous yet thrilling world of AI in 2023 and to cast an eye toward what 2024 may hold. But first, I wanted to say thank you! Each and every one of you have shared this newsletter to where now thousands of readers are enjoying our updates, day in and day out. Stay tuned and be sure to check out the end of this article for some exciting updates of whats to come.
2023 has been a pivotal year in the realm of AI, marked by groundbreaking developments, emerging challenges, and significant policy debates. Let's unpack the major stories and trends that shaped the AI landscape:
Generative AI's Meteoric Rise
Generative AI, particularly large language models (LLMs) like OpenAI's GPT-4, took center stage in 2023. These models showcased remarkable abilities across diverse domains, including healthcare, education, and creative arts. They were even capable of answering complex medical exam questions, generating persuasive political messages, and choreographing dance animations.
AI in Education
A significant conversation centered around AI's impact on teaching and learning. The AI+Education Summit highlighted the potential benefits and risks of AI in education, discussing personalized support for teachers and changes in learning methodologies. However, concerns were raised about the lack of cultural diversity in AI outputs and the generation of incorrect responses.
AI Ethics and Transparency
As AI became more integrated into daily life, ethical concerns about transparency and bias grew. Tools like Stanford's DetectGPT, designed to differentiate between human and AI-generated text, emerged as essential in maintaining integrity in information dissemination. The Foundation Model Transparency Index (FMTI) was developed to evaluate AI models on aspects of transparency, indicating a need for improvement across the industry.
AI in Healthcare
Research on AI's application in healthcare revealed mixed results. While initial responses from LLMs like GPT-3.5 and GPT-4 to clinical questions were generally safe, they often disagreed with known answers, highlighting the need for more refinement and evaluation in healthcare applications.
Policy Developments
The EU AI Act emerged as a major policy milestone, poised to become the first comprehensive legal framework for AI. It involved negotiations among key EU institutions and set the stage for more structured AI governance. How the US will respond has yet to be seen. So far it’s been all talk and no action. Which, knowing how our political system in the US is set up, that may be a great thing. One thing is for certain, we need a diverse set of opinions from citizens and open-source developers before just relying on big tech.
AI Detectors and Biases
AI detectors, designed to identify AI-generated content, were found to be biased against non-native English writers, raising ethical concerns about their use in educational and professional settings. We also found out that it’s virtually impossible to detect and many businesses, including OpenAI gave up on developing AI-detecting tools. The same applies to any watermark techniques.
AI's Role in Political Discourse
AI-generated messages were found to be as effective as those written by humans in influencing political opinions, raising concerns about AI's potential misuse in political contexts. We are in for a wild ride in the US with AI ads and even AI cold calling tools that politicians are using to encourage people to get out and vote for them are flooding the phones nationwide.
AI and Choreography
The development of a generative AI model called Editable Dance Generation (EDGE) by Stanford researchers marked a novel application of AI in choreography, suggesting new creative possibilities.
Challenges and Critiques
Despite the excitement around generative AI, several challenges were identified:
- Fundamental Flaws in Language Models: Issues like fabricating information and biases in gender, ethnicity, and politics were notable. Generative models often showed unpredictable behavior, necessitating attempts to guide them toward more desirable outcomes.
- Carbon Footprint: The energy consumption of AI models came under scrutiny, with studies revealing significant energy requirements for tasks like image generation.
- AI Doomism: The debate about AI posing existential risks to humanity gained traction, with prominent figures in the field expressing varied opinions.
- Regulatory Advances: The conversation around AI policy and regulation intensified, leading to significant policy developments like the EU AI Act and the White House's executive order on AI.
AI Industry Trends
The AI industry saw an influx of new models from major tech companies, although no single application became an overnight success. The search for a universally appealing AI product continued, with a focus on embedding AI in practical tools to enhance productivity.
As we turn towards 2024, these developments from 2023 set the stage for further innovation, challenges, and policy discussions in the AI arena. The upcoming year promises to be crucial in determining the real value and direction of generative AI, as well as the evolution of policies and ethical frameworks guiding its development and use.
2024: The Road Ahead
OpenAI's Evolution: OpenAI is slated to pivot from a research entity to a more product-focused company. This isn't just a shift; it's a potential game-changer for how AI tools are commercialized.
Beyond the Niche: AI is set to move from behind-the-scenes to front-and-center in fields like insurance and media. It's not just about doing things differently; it's about reimagining these domains.
Rethinking LLMs: The limits of large language models will come into sharper focus, possibly ushering in an era of more specialized, multimodal models. This isn't just evolution; it's a necessary sophistication of the technology.
Apple's AI Foray: Apple's potential entry into AI could be a catalyst for integrating AI into everyday life, leveraging its vast ecosystem.
AI and the Law: Legal challenges are on the horizon, particularly regarding AI's use in sensitive areas like hiring and lending. This is more than just legal jargon; it's about setting precedents for AI's societal role.
Elections and AI: The role of AI in political narratives and voter influence is a topic to watch in the 2024 elections. This isn't just about technology; it's about the fabric of democracy.
AIdeations' Next Steps
As we step into 2024, AIdeations is poised to evolve alongside these trends. With initiatives like bi-weekly Q&A sessions and weekly video content on AI developments, we're not just following the story; we're part of it. Your engagement is key to shaping our journey and ensuring AIdeations remains at the forefront of AI discourse.
2023 was a year of both breakthroughs and challenges in AI, setting the stage for an even more dynamic 2024. Let's continue to navigate this landscape together, staying informed and critically engaged.
New York Times vs. OpenAI: A Landmark Legal Battle Over AI and Copyright
In what may become a defining moment for the relationship between artificial intelligence and copyright law, The New York Times has initiated a lawsuit against OpenAI, the creators of ChatGPT, alleging copyright infringement. The case, marked by its complexity and potential implications, delves into uncharted legal territory.
The Core of the Complaint
The lawsuit filed by The New York Times underscores the principle of 'access and substantial similarity.' The complaint asserts that ChatGPT, trained on a vast array of internet data, including a significant portion from The New York Times itself, mirrors the newspaper's content too closely. This raises fundamental questions about the nature of AI-generated content and its originality.
Visual Evidence: A Powerful Tool
One of the most striking aspects of the case is the visual presentation of the alleged copyright infringement. The complaint features a side-by-side comparison, with purportedly copied text marked in red and original GPT-generated content in black. This stark visual contrast is designed to influence not only the legal outcome but also public perception.
The New York Times: A Strong Plaintiff
The Times positions itself not just as a creator of news articles but as a beacon of creativity and original journalism. The lawsuit highlights specific examples of in-depth reporting, such as an exposé on taxi lending, to illustrate that their work transcends mere news reporting; it involves a creative process deserving of protection.
Negotiation Failures and Potential Damages
The case is complicated by the fact that OpenAI has previously licensed content from other media outlets, such as Politico. However, negotiations with The New York Times apparently failed, which could potentially increase the damages sought by the newspaper, especially given OpenAI's growing profits and increasing visibility of this issue.
Narrative of Public Good vs. Profit-Driven Tech
The complaint casts OpenAI in a negative light, portraying it as a profit-driven entity in contrast to the societal value of journalism. This narrative could play a significant role in the courtroom, balancing the importance of copyright against technological innovation.
Allegations of Misinformation: A Clever Twist
Adding a unique dimension to the lawsuit are claims of misinformation, focusing on instances where ChatGPT allegedly fabricated elements of New York Times articles. This aspect of the case taps into public fears about AI-generated misinformation, potentially strengthening the newspaper's position.
Legal Expertise: A Strategic Move
The New York Times has enlisted the services of Susman Godfrey, a law firm with a notable track record in taking on tech companies. This move indicates the strategic nature of the lawsuit, distinguishing it from other, more opportunistic legal challenges against OpenAI.
A Watershed Moment for AI and Copyright
As the legal battle unfolds, its outcome could set a precedent for how AI innovations are treated under copyright law. The stakes are high, encompassing not only the future of AI development but also the protection of creative content in the digital age. The decision in this case could shape the landscape of AI and copyright for years to come.
Samsung Unveils AI-Powered Fridge for 2024
Samsung's latest kitchen marvel – the 2024 Bespoke 4-Door Flex Refrigerator with AI Family Hub+. This isn't just any fridge; it's a tech powerhouse tailored for the modern kitchen. With an internal camera that identifies what's inside, it goes beyond mere cooling. It's about making life easier and healthier. You've got ingredients, and it's got suggestions – recipes generated based on what's available in your fridge. And for those with specific dietary preferences or needs? This fridge is your new best friend, offering personalized recipes whether you're vegan, pescatarian, or anything in between.
But there's more to it than just playing pantry detective. The integration with the Samsung Health profile adds a new layer of personalization. It's like having a dietitian in your kitchen, recommending meals that align with your health goals. This synergy between diet, health, and technology is a leap forward in smart home innovation. And let's not forget the convenience of a 32-inch Family Hub touchscreen that mirrors your Galaxy phone. Imagine managing your meal plans or catching up on videos right from your fridge door.
However, it's not without its limits. The fridge's Vision AI can recognize up to 33 food items, which means it might not catch every exotic ingredient you throw its way. Users also need to manually input expiration dates for food items, but the fridge smartly notifies you when they're about to go bad. Samsung is positioning this fridge as more than a household appliance; it's a central piece in a broader ecosystem of AI-driven kitchen gadgets, including the upcoming Anyplace Induction Cooktop. This integration of various devices through the Samsung Food app hints at a future where our kitchens are not only smart but also interconnected, elevating the cooking experience to new heights.
2024: The Year of Data and AI Evolution - 11 Key Trends Reshaping the Enterprise Landscape
Alright, let's set the stage for the thrilling world of AI and data in 2024. Picture this: It's a year that's shaping up to be the equivalent of a tech renaissance, a time when our approach to data and AI undergoes a transformation so profound, it's like watching a black and white film burst into color. We're on the cusp of witnessing groundbreaking shifts that promise to redefine how businesses interact with data, make decisions, and drive innovation. From the evolution of database languages to the strategic reshuffling in corporate data roles, each of these 11 points offers a glimpse into a future where data isn't just a resource; it's the very lifeblood of enterprise growth and technological advancement. So, fasten your seatbelts and prepare for a journey through the 11 pivotal trends set to revolutionize the AI-centric enterprise landscape in 2024. 🚀🌐💡
1. Goodbye SQL, Hello Agility: SQL, the once go-to database language, is losing its charm. We're moving towards more flexible databases that play nice with modern tech like IoT and AI. It's out with the old, in with the new!
2. Vector Databases Take the Lead: Watch out for vector databases becoming the tech darling of 2024. They're perfect for handling complex AI tasks like image recognition and natural language processing. Vector databases are essentially the Swiss Army knives of the data world.
3. Unleashing AI on Data Lakes: Companies are sitting on data goldmines and barely scratching the surface. In 2024, they're going to let AI loose on these data lakes to uncover hidden insights, from health trends to retail patterns. It's like finally using that metal detector you bought on a whim.
4. The Pain of Poor Data Management: If your data isn't in good shape, brace yourself. Bad data will be the Achilles' heel for AI implementations, leading to misguided decisions and automation gone wrong. It's time to clean house!
5. Cloud FinOps to the Rescue: With cloud costs skyrocketing, FinOps teams will be the superheroes of 2024, optimizing data pipelines and cutting down on wasteful spending. It's all about getting more bang for your buck.
6. Intent Data is a Must-Have: For those in sales and marketing, intent data is shifting from a luxury to an essential tool. It's like having a secret decoder ring that reveals what customers really want.
7. Data vs. Business Teams on AI: Expect some friction as data and business teams grapple with integrating AI into their workflows. It's a classic case of tech meets tradition, and finding the right balance will be key.
8. Real-Time AI Analytics: Enterprises will get a major upgrade with AI-powered real-time data analytics, leading to smarter decisions and cost savings. Imagine having a super-fast data-crunching assistant – that's 2024 for you.
9. Knowledge Graphs to the Rescue: Say goodbye to the nightmare of data silos. Knowledge graphs will make it easier to navigate and leverage diverse data sources, streamlining data management like never before.
10. AI Reinvents Data Management: AI is not just a tool; it's transforming how businesses manage and use data. The challenge will be to balance data protection with the need for insightful decision-making.
11. The Rise of the Chief Data Officer: The CDO role is becoming a stepping stone to becoming a CIO. Understanding data dynamics is now crucial for climbing the corporate ladder in the tech world.
There you have it, 11 key points that paint a picture of what 2024 holds in the realm of AI and data. It's going to be a year where data isn't just king – it's the entire kingdom! 🌐👑
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Title:
Authors:
Dahyun Kim, Chanjun Park, Sanghoon Kim, Wonsung Lee, Wonho Song, Yunsu Kim, Hyeonwoo Kim, Yungi Kim, Hyeonju Lee, Jihoo Kim, Changbae Ahn, Seonghoon Yang, Sukyung Lee, Hyunbyung Park, Gyoungjin Gim, Mikyoung Cha, Hwalsuk Lee, Sunghun Kim
Executive Summary:
The research paper introduces a novel technique called depth up-scaling (DUS) for efficiently and effectively scaling up base Large Language Models (LLMs). Unlike the mixture-of-experts (MoE) method, DUS simplifies the training and inference processes. The study demonstrates the application of DUS in developing SOLAR 10.7B, a 10.7 billion parameter LLM. This model shows superior performance in various natural language processing (NLP) tasks and outperforms existing open-source pretrained LLMs like Llama 2 and Mistral 7B. A specialized variant, SOLAR 10.7B-Instruct, is also presented, which is fine-tuned for instruction-following capabilities, surpassing the Mixtral-8x7B model. SOLAR 10.7B is made publicly available under the Apache 2.0 license, promoting widespread access and application in the LLM field.
Pros:
1. Innovative Scaling Technique: Utilizes a novel depth up-scaling method that simplifies the scaling process of LLMs.
2. Superior Performance: Demonstrates enhanced performance in various NLP tasks compared to existing models.
3. Public Accessibility: Released under the Apache 2.0 license, ensuring broad access and potential for widespread application.
4. Specialized Variant: Includes SOLAR 10.7B-Instruct, fine-tuned for instruction-following, enhancing its utility in specific scenarios.
Limitations:
1. Computational Requirements: High computational demands for training and inference may limit accessibility for those with constrained resources.
2. Data Bias and Contamination: Like all machine learning models, it's susceptible to biases inherent in its training data.
3. Interpretability Challenges: The complexity of SOLAR 10.7B can pose issues in interpretability and explainability.
4. Language and Domain Limitations: Effectiveness may vary across languages, especially those with fewer resources, and in specialized domains.
5. Environmental Concerns: Significant energy consumption for training and operation raises environmental sustainability issues.
6. Need for Specialized Fine-Tuning: While the SOLAR 10.7B-Instruct variant shows improved performance, it still requires task-specific fine-tuning for optimal performance in specialized applications.
Use Cases:
- Natural Language Processing (NLP) Applications: Particularly useful in scenarios requiring advanced language understanding and processing.
- Instruction-Following Tasks: SOLAR 10.7B-Instruct is specifically tailored for scenarios that need precise following of instructions.
- Research and Development: Its public availability under an open-source license makes it a valuable tool for collaborative research and development in LLMs.
Why You Should Care:
SOLAR 10.7B represents a significant advancement in the field of LLMs, offering a simpler yet effective method for scaling these models. Its superior performance in various NLP tasks and the availability of a specialized variant for instruction-following make it a compelling choice for a wide range of applications. The model's public availability encourages broad usage and experimentation, potentially leading to innovations in language understanding and AI development. However, awareness of its limitations, including computational demands, potential biases, and environmental impact, is crucial for its responsible and effective utilization.
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