Gen AI for Business # 21

Welcome to this week's newsletter, where we dive into a roundup of all the latest developments in AI. From regulatory moves to regional growth and new partnerships, it's been another busy week for Gen AI. We’ll cover some interesting updates, including OpenAI’s plans for a potential $2,000 subscription model for its most advanced LLMs, Nvidia’s dramatic market drop, and the UK government's AI training for civil servants. On the regulatory front, concerns about AI's ethical use continue to shape policies worldwide, while in the U.S., AI's role in job markets and deepfakes remains a hot topic. Regionally, AI infrastructure continues to expand, as seen with Google's investment in Uruguay, while new partnerships and investments are driving AI innovation forward.?

Additionally, you'll find two highly useful tables—one comparing enterprise AI solutions and another outlining content moderation approaches across popular AI models.

What Stood Out to Me:

One thing that really caught my attention this week is how the enterprise AI market is becoming more deliberate in its approach. Companies are no longer rushing into AI adoption out of fear of missing out; instead, they’re focusing on strategic integration, customization, and aligning AI solutions with specific business goals.?

We’re also witnessing a shift in the investment landscape, with tech giants like Microsoft and Amazon leading the charge and reshaping the venture capital game. Traditional VCs are finding it harder to compete, leading them to focus on niche markets or early-stage startups, while large corporations are gaining control over the most promising AI innovations.

If you enjoyed this letter, please leave a like or a comment and share! Knowledge is power.?

Let’s dive in.

Thank you, Eugina

News about models and everything related to them

The xAI team launched Colossus, the world’s most powerful AI training cluster with 100,000 GPUs, and it’s already set to double its capacity. Google DeepMind’s research shows that smaller AI models can outperform larger ones when trained efficiently. Aleph Alpha has pivoted from developing large language models to providing AI support services, focusing on helping businesses integrate AI tools. DeepMind’s GenRM is enhancing model accuracy by having AI verify its own outputs. A survey by Muah AI reveals that over 85% of users prefer uncensored AI models for creative freedom, though some express concerns about risks. A comparison table outlines how popular AI models like OpenAI’s ChatGPT, Anthropic’s Claude 2, and Muah AI manage content moderation, offering different levels of censorship.?

• https://threadreaderapp.com/thread/1830650370336473253.html highlights the launch of the Colossus 100k H100 training cluster by the @xAI team, which was brought online over the weekend after being completed in just 122 days. Described as the most powerful AI training system globally, Colossus currently boasts 100,000 H100 GPUs and is set to double its capacity to 200,000 GPUs, including 50,000 H200s, in the coming months. The rapid development and ambitious expansion plans demonstrate significant advancements in AI infrastructure and capabilities. The team credited Nvidia and various partners and suppliers for their excellent collaboration in achieving this milestone.

• Can Smaller AI Models Outperform Giants? This AI Paper from Google DeepMind Unveils the Power of 'Smaller, Weaker, Yet Better' Training for LLM Reasoners - MarkTechPost discusses a recent AI research paper from Google DeepMind that explores the potential of smaller AI models to outperform larger ones under certain conditions. The study suggests that smaller, less complex models can achieve competitive or even superior performance compared to giant models when trained more effectively. This is particularly true for specific reasoning tasks where the training approach and data quality play a critical role. The research indicates that using targeted training strategies and high-quality datasets can enhance the capabilities of smaller models, allowing them to perform well without the computational overhead and resource demands of larger models. This finding challenges the prevailing notion that bigger models are always better, highlighting that model efficiency and tailored training can yield significant improvements in performance.

• ?German LLM maker Aleph Alpha pivots to AI support | TechCrunch Aleph Alpha, a leading German AI company known for its work on large language models (LLMs), is pivoting from competing in the advanced LLM space to focusing on AI support services. Despite securing over $500 million in funding from German industrial giants in 2023, the company has recognized the challenges of competing against tech giants like OpenAI. CEO Jonas Andrulis explained that maintaining a competitive LLM is no longer a sustainable business model for them. Instead, Aleph Alpha has introduced PhariaAI, a product designed to help businesses and the public sector leverage AI tools without needing to own or develop the underlying technology themselves. This pivot allows them to serve a broader range of industries while moving away from the expensive race to dominate LLM development. Do you think that other LLM companies will have to evolve unless they have deep pockets??

• ICYMI DeepMind's GenRM improves LLM accuracy by having models verify their own outputs | VentureBeat DeepMind's GenRM enhances the accuracy of large language models (LLMs) by implementing a mechanism where the models verify their own outputs. This self-checking approach allows the AI to improve reliability by comparing generated results with various reference points or patterns, leading to more accurate outputs. The system can identify and correct errors autonomously, addressing common issues like hallucinations in LLM responses.?

• Over 85% of Users Prefer Uncensored LLM, Muah AI Survey Reveals | Morningstar Muah AI's (they provide uncensored platform) September 2024 survey of 25,000 users revealed that over 85% prefer using uncensored large language models (LLMs), citing frustrations with content filters that stifle creativity, problem-solving, and natural dialogue. Users, especially those engaging in creative fields like storytelling, found restrictions limiting. However, 14.6% of respondents expressed concerns over the potential for uncensored AI to promote harmful behavior, emphasizing the need for balance. The survey indicates a growing demand for personalized AI experiences where users control moderation levels.?

Here is a table of most popular models and if they are censored or uncensored to help you understand what is being censored and type of moderation being used.?

Gen AI news from different industries

Auditors are cautiously embracing generative AI, seeing the potential for efficiency while stressing the importance of professional judgment and the need for clear policies to guide its use. In supply chains, AI promises to revolutionize operations, but companies are struggling with poor data quality and a lack of skilled personnel, slowing adoption. B2B marketing in 2024 is all about AI, with trends like personalized marketing, account-based strategies, and an increased focus on data privacy shaping the landscape. Retail giants like Amazon, Walmart, and Sephora are using AI to enhance shopping experiences, from personalized recommendations to virtual try-ons, making the customer journey smoother and more interactive. AI translation tools are becoming smarter and more context-aware, promising to break down language barriers across industries like e-commerce and healthcare. Google’s new AI research is looking to detect illnesses through vocal biomarkers, showing promise in non-invasive diagnostics, while the healthcare industry grapples with the challenge of regulating AI use to ensure patient safety and transparency. Finally, generative AI is making waves in space, offering game-changing improvements in mission planning, satellite operations, and defense, bringing the future of space exploration closer to reality.

Audit

• Embracing change: Auditors' views on generative AI discusses how auditors view the integration of generative AI in their field. While AI offers opportunities for enhanced predictive analytics and efficiency, concerns remain about data security, accuracy, and over-reliance on technology. Auditors emphasize a balanced approach to using AI, ensuring it supports rather than replaces professional judgment. There is a call for clear policies and guidelines to govern AI's ethical use within audit firms.?

?

Supply chain

• The Problem Behind AI Implementation in the Supply Chain highlights the challenges of implementing AI in the supply chain, focusing on issues like poor data quality, fragmented systems, and the lack of skilled personnel to manage AI technologies. Many companies struggle with integrating AI due to inconsistent data and insufficient expertise, leading to ineffective deployments that don't align with business goals or deliver expected returns. Additionally, resistance to change and skepticism about AI's reliability further hinder adoption. The article suggests improving data quality, investing in training, and developing a clear AI strategy to overcome these obstacles and realize AI's potential in supply chain management.

Marketing?

• Marketing technology: Top trends shaping B2B marketing in 2024! - Brand Wagon News | The Financial Express outlines the top trends shaping B2B marketing in 2024, emphasizing the growing role of technology in driving marketing strategies. Key trends include the increased use of AI and machine learning for data analysis, customer segmentation, and personalized marketing efforts. AI tools are enabling marketers to better understand customer behavior, predict trends, and tailor content to individual needs, enhancing engagement and conversion rates. Another significant trend is the rise of account-based marketing (ABM), where marketing efforts are more targeted and personalized toward high-value accounts. This approach is becoming more sophisticated with the use of advanced analytics and automation, allowing for more precise targeting and improved ROI. The article also notes the growing importance of data privacy and security, as businesses prioritize compliance with regulations and build trust with customers through transparent data practices. Additionally, there is a focus on integrating multiple marketing channels to create a seamless and consistent customer experience. Marketers are leveraging a mix of digital and traditional channels to engage customers at various touchpoints, ensuring a cohesive and effective marketing strategy.

?

Retail

• What the retail industry has learned from AI shopping assistants in 2024 highlights several major retailers that are leveraging AI shopping assistants to enhance the customer experience. Notably, companies like Amazon, Walmart, and Sephora are leading the charge in adopting these AI-driven technologies. Amazon uses AI to provide personalized recommendations, streamline purchasing, and enhance the overall shopping experience through its voice assistant, Alexa which is due for an overhaul with Calude. Walmart is utilizing AI to optimize customer service and improve inventory management, creating a more seamless shopping experience. Sephora offers virtual try-on tools powered by AI, allowing customers to visualize products before purchasing, providing tailored beauty recommendations based on customer preferences and past purchases.

Retailers are increasingly using AI to create more interactive and engaging experiences, such as virtual try-ons and tailored product suggestions based on user behavior and preferences. The integration of AI also improves inventory management and optimizes supply chain operations by predicting demand more accurately.

Translation

• The Future of AI Translation: What to Expect - Business AI translation tools, powered by natural language processing (NLP) and machine learning, are expected to become even more accurate and context-aware. In the future, AI translation will go beyond literal word-for-word translations and be able to better understand cultural nuances, tone, and intent, which are essential for effective communication across languages. Improvements in contextual understanding will allow AI to produce more human-like translations, making it easier for businesses and individuals to communicate globally. AI translation is poised to play a critical role in industries like e-commerce, healthcare, and education, where real-time, multilingual communication is becoming increasingly important. The article also highlights the potential for AI-powered translation to break down language barriers in customer service and cross-border collaborations, creating more seamless interactions in various sectors. While challenges like data privacy and the potential for biases in translation models remain, ongoing innovations suggest a future where AI translation is more reliable, inclusive, and capable of handling complex linguistic tasks.

Health

• Google is working on AI that can hear signs of sickness | TechCrunch Google is developing AI models designed to detect signs of illness by analyzing changes in a person’s voice. The AI is focused on identifying vocal biomarkers associated with health conditions like respiratory infections, cardiovascular issues, and even mental health disorders. By analyzing subtle shifts in speech patterns, such as tone, pitch, and breath control, the AI could potentially identify early signs of disease. Google’s research is part of its broader efforts in healthcare technology, following initiatives like AI for medical imaging and disease prediction. While the technology is still in the experimental phase, it holds promise for non-invasive health diagnostics.?

• How to Regulate Generative AI in Health Care explores the challenges and strategies for regulating generative AI in healthcare. As AI becomes more integrated into healthcare applications—such as diagnostics, treatment planning, and patient communication—there is an urgent need for clear and effective regulations. The key concerns include patient safety, data privacy, and bias in AI models. The article suggests a multi-pronged approach: establishing regulatory standards that focus on transparency, ensuring AI systems are explainable, and implementing continuous monitoring to detect biases and errors. Collaboration between regulators, healthcare providers, and AI developers will be essential in creating a regulatory framework that fosters innovation while protecting patients.?

Space

• Generative AI is Now in Space. Here’s Why That’s a Big Deal explores the growing significance of generative AI in space operations, emphasizing how it can revolutionize space exploration and defense. Generative AI’s ability to analyze vast amounts of data, predict satellite trajectories, and automate spacecraft operations can enhance mission planning, improve situational awareness, and reduce human error. In defense applications, AI can assist with real-time decision-making and optimize satellite communications and surveillance. The article highlights that generative AI’s capacity to process complex data faster than traditional systems makes it a critical tool in advancing both civilian and military space efforts.??

?

Regional and regulatory updates

Developing ChatGPTs for Indian languages is no easy task, but it’s a long-term effort focusing on capturing the rich diversity and cultural nuances of India's many dialects. California is making moves to protect performers’ digital rights by requiring explicit consent before AI can replicate their likeness after death. In Ghana, AI is modernizing herbal medicine education, helping students better understand and improve traditional treatments. China’s AI advancements are reaching for the moon—literally—by incorporating AI models in lunar exploration. U.S. states and businesses are tackling the deepfake challenge head-on with new laws and tools aimed at improving transparency and combatting misinformation. UK businesses are jumping into AI out of fear of missing out, but they risk poor results without a clear strategy. South Africans are embracing AI, but they’re also calling for more training to fully unlock its potential and drive economic growth. The UK just signed its first international AI treaty, setting the stage for global safeguards on ethical and transparent AI use. The EU AI Act is shaking up how American businesses approach AI, forcing them to comply with new rules while opening the door to ethical leadership. Google’s new data center in Uruguay is a big step forward for AI in Latin America, bridging the compute divide and powering future innovations.

• Marathon, not a sprint: Developing authentic ChatGPTs for Indian languages - The Hindu – discussing the challenges and efforts in developing AI models like ChatGPT for Indian languages. It highlights the importance of capturing linguistic diversity and cultural nuances, and the need for significant resources and collaboration to build accurate, context-aware language models for India's many dialects. The development process is described as a long-term effort, focusing on authenticity and inclusivity in AI language tools. The development process for authentic AI models like ChatGPT for Indian languages involves several key steps. Researchers must gather diverse and high-quality datasets that represent the linguistic and cultural nuances of various Indian languages and dialects. This includes handling script variations and regional idioms. Collaborative efforts among linguists, data scientists, and AI experts are crucial to refine these models. The process also requires addressing challenges like data scarcity and ensuring models are inclusive, unbiased, and context-aware, which is a long-term endeavor.

• California Passes Law Requiring Consent for AI Digital Replicas of Dead Performers The article from Variety discusses the new SAG-AFTRA agreement in California, which requires explicit consent from performers before using AI to replicate their likenesses after death. This measure is part of broader efforts to protect digital rights and prevent unauthorized use of deceased performers' images or voices. The regulation reflects growing concerns about the ethical use of AI in entertainment and aims to safeguard performers' legacies and rights in the digital age. The SAG-AFTRA agreement in California is currently on pause because there is ongoing debate and negotiations over how AI should be used to replicate deceased performers' likenesses. The pause allows for further discussion on the ethical implications, consent requirements, and potential financial arrangements related to using AI in this way. The goal is to ensure performers' digital rights are protected and that any use of their likeness posthumously is done ethically and with explicit permission.?

• Artificial Intelligence introduce in herbal medicine and naturopathic education - The Business & Financial Times details how AI is being integrated into herbal medicine and naturopathic education in Ghana, with institutions like the Kwame Nkrumah University of Science and Technology (KNUST) leading the initiative. AI tools are used to analyze herbal treatments and patient data, improving diagnostic accuracy and treatment plans. The initiative aims to modernize traditional medicine practices by incorporating AI for better educational outcomes and personalized healthcare solutions. This approach also seeks to enhance the understanding of herbal efficacy and safety.

• ?Chinese scientists release AI model for lunar exploration - People's Daily Online reports on China's advancements in artificial intelligence (AI) research, highlighting the nation's efforts to lead in AI development through significant investments and policy support. Key initiatives include enhancing AI education, fostering innovation in AI technologies, and building infrastructure to support AI research and deployment. The government aims to integrate AI across various sectors, such as healthcare, finance, and transportation, positioning China as a global leader in AI innovation.?

• AI Briefing: How state governments and businesses are addressing AI deepfakes - Digiday discusses how U.S. state governments and businesses are tackling the challenges posed by AI deepfakes and misinformation. California's proposed AI Transparency Act aims to enhance transparency for AI-generated content. Several states have implemented laws requiring disclosures on AI-created political ads. Businesses like Pindrop and McAfee have developed tools to detect deepfakes, helping combat misinformation. Public campaigns and partnerships, such as those in Washington state, further educate citizens about deepfake risks. These efforts reflect a growing focus on addressing AI's potential misuse in politics and media.??

• UK businesses say that fear of missing out is driving AI adoption | TechRadar reveals that fear of missing out (FOMO) is driving AI adoption among UK businesses, with many adopting AI technologies to keep up with competitors rather than based on a strategic plan. This reactive approach has led to a surge in AI investments but often without a clear strategy, risking poor integration, inadequate training, and low ROI. The article advises that for AI adoption to be effective, businesses need to move beyond FOMO, develop comprehensive strategies, and align AI implementation with their specific needs and long-term goals.??

• South Africans embrace AI, but seek training highlights that while South Africans are increasingly embracing AI technologies, there is a strong demand for more training and education on how to use these tools effectively. Many people recognize the potential of AI to enhance productivity, improve services, and drive economic growth, but they also express concerns about a lack of skills and understanding needed to fully leverage AI's capabilities. The article notes that businesses and individuals are seeking training programs to upskill and adapt to the evolving technological landscape. To maximize the benefits of AI adoption, there is a call for increased investment in education and training initiatives that can bridge the knowledge gap and ensure broader, more effective use of AI across various sectors in South Africa.??

• UK signs first international treaty to implement AI safeguards | Artificial intelligence (AI) | The Guardian The UK signed its first international AI treaty with 25 other countries, led by the OECD (Organisation for Economic Co-operation and Development). The agreement focuses on establishing global safeguards for artificial intelligence, promoting transparency, safety, and ethical standards in AI development and use. The treaty emphasizes accountability in AI systems, encouraging countries to adopt frameworks that ensure AI is used responsibly, particularly in areas like defense and critical infrastructure.??

• What the EU AI Act Means for American Businesses The EU AI Act introduces stringent regulations aimed at ensuring the responsible use of AI, focusing on transparency, safety, and data privacy. For American companies operating in Europe or interacting with European customers, compliance with the Act will be essential. The legislation classifies AI systems based on risk levels, from minimal to high risk, and imposes stricter requirements on higher-risk applications like healthcare and law enforcement. Businesses must adapt by conducting risk assessments, ensuring transparency in AI systems, and adhering to new reporting requirements. The article stresses that while compliance may require significant adjustments, it also opens opportunities for businesses to lead in ethical AI practices globally.?

• Google Starts Construction of $850M Data Center in Uruguay This marks Google’s first data center in Latin America, highlighting the region’s growing importance in the global digital infrastructure landscape. The new facility will support Google’s cloud operations and AI services, expanding its ability to serve customers across the Americas. The investment is part of Google’s broader strategy to build sustainable, high-performance data centers globally. The Uruguay facility will feature renewable energy sources, aligning with Google’s commitment to carbon-neutral operations by 2030.?

Google’s investment in Uruguay signals an expansion of AI compute infrastructure in Latin America, a region classified as Compute South. This development could help bridge the compute divide by bringing high-performance AI compute resources closer to the region, enabling more local AI development and deployment. It also supports the broader trend of increasing the global distribution of AI infrastructure, which can impact governance, accessibility, and innovation.

??

News and Partnerships

Lenovo is gearing up to release affordable Copilot Plus PCs this month, packed with AI-driven features to boost productivity and streamline tasks. Microsoft is stepping up the game with generative AI in Bing, aiming to provide users with more relevant search results to compete with Google. Apple is diving into the AI world with its new Apple Intelligence, offering features like AI-powered writing suggestions and image tools, available soon on its latest devices. TIME’s 100 Most Influential People in AI for 2024 celebrates the innovators and leaders shaping the future of AI across industries. Anthropic’s Claude for Enterprise is designed for businesses looking to integrate AI with a focus on safety, transparency, and efficiency, perfect for industries like healthcare and finance. Finally, our table compares top enterprise AI solutions, from Google Cloud AI to Salesforce Einstein, offering insights into costs and key features for businesses exploring AI integration.

• Lenovo leak shows cheaper Copilot Plus PCs coming this month - The Verge discusses a recent leak about Lenovo's upcoming AI-focused products, revealed at IFA 2024. Lenovo is reportedly working on a new AI platform called "Copilot Plus" designed to enhance productivity and user experience on its PCs. Copilot Plus is expected to leverage generative AI to provide advanced features, including real-time transcription, personalized recommendations, and automated task management. This platform aims to integrate seamlessly with existing Lenovo devices, potentially making them more intuitive and efficient for users. The leak also mentions that Lenovo plans to introduce several new PCs equipped with the Copilot Plus platform, positioning them as powerful tools for both professional and personal use. These PCs will likely feature upgraded hardware to support the AI capabilities, including better processors and enhanced memory.?

• ?Microsoft introduces gen-AI to Bing search, similar to Google's AI Overviews highlights Microsoft's introduction of generative AI to Bing Search, similar to Google's AI Overviews. This feature enhances search results with detailed, AI-generated summaries, aiming to provide users with more relevant and comprehensive answers directly on the search page. The update is part of Microsoft's strategy to boost Bing's competitiveness by offering a more intuitive and informative search experience using AI.

Microsoft's introduction of generative AI to Bing Search can be seen as part of its strategy to challenge Google's market position. By enhancing Bing with AI-generated summaries and improving user experience, Microsoft aims to attract more users and provide a competitive alternative to Google. This move could influence antitrust discussions by demonstrating that there are viable competitors in the search market, potentially affecting how regulators view Google's market power and its impact on competition.

• ?What Is Apple Intelligence? Everything To Know About iPhone 16 AI Features - CNET Apple Intelligence, part of iOS 18, iPadOS 18, and MacOS Sequoia, marks Apple's significant foray into generative AI. Designed to assist users with tasks like writing and creative projects, it is currently available in developer beta. Key features include AI-powered writing suggestions for documents and emails, image tools like Clean Up to remove unwanted elements, and enhanced Siri capabilities with more natural conversations and contextual understanding. Siri also receives a new interface and can now generate summaries across apps such as Messages, Mail, and Notes. Apple Intelligence is expected to roll out later in 2024, initially limited to devices with newer chipsets, including the iPhone 15 Pro and iPads and Macs with M1 or later chips. Apple highlights privacy by processing many AI tasks on-device, while also allowing access to third-party AI tools like ChatGPT through Siri.??

• The 100 Most Influential People in AI 2024 | TIME The TIME100 AI 2024 list highlights the most influential people shaping the future of artificial intelligence. This collection features innovators, researchers, and leaders from various industries who are making significant contributions to AI technology and its applications. The list includes AI developers, ethical researchers, and policy makers who are at the forefront of AI advancements, driving change in areas like healthcare, business, education, and more.??

• Claude for Enterprise \ Anthropic ? announces Claude for Enterprise, a new AI product from Anthropic designed to help businesses leverage advanced AI capabilities for enterprise needs. Claude offers tools to enhance productivity, automate tasks, and improve decision-making within organizations. With a focus on safety, reliability, and ethical AI use, Claude is tailored for industries requiring high compliance standards, including finance, healthcare, and legal. Anthropic emphasizes that Claude is designed to integrate seamlessly with existing systems, offering robust AI capabilities while maintaining transparency and data privacy. The tool’s features aim to reduce inefficiencies and streamline workflows for enterprise users. Learn more here: Enterprise \ Anthropic ??

Here is a comparison table of the most common enterprise Gen AI solutions available today.

The enterprise AI market is experiencing rapid growth, with major players like Google, Anthropic, IBM, and Microsoft leading the charge. According to research from McKinsey and Grand View Research, AI adoption in enterprises is projected to grow by 38% annually, driven by automation, predictive analytics, and cloud computing. Within 1 year, AI will become more accessible for medium enterprises, while in 3 years, multimodal AI and AI-driven decision-making will be standard in sectors like healthcare and finance. By 5 years, AI will be deeply integrated into all aspects of business operations, with generative AI and explainable AI becoming critical tools for innovation and decision-making. Regulatory frameworks will also strengthen, shaping the ethical use of AI in business.

Gen AI for Business Trends, Concerns, and Predictions:?

MIT researchers warn that AI is too 'sociopathic' to give financial advice, urging stricter ethical guidelines and human oversight to avoid harm. The risk of "model collapse" is real, as AI models trained on synthetic data could degrade in performance over time, emphasizing the need for a balance between real-world and synthetic data. A research team has proposed a solution to the "model collapse" problem by creating a feedback loop that combines synthetic and real-world data, ensuring AI models remain accurate and reliable. The battle over web crawling is heating up, with companies leveraging this technique to improve AI models, but concerns about data quality and privacy remain. AI-assisted search is changing the game for content marketers, pushing them to adapt their SEO strategies and create more targeted content based on user behavior. AI may not replace human artists anytime soon, as it lacks the emotional depth and cultural understanding that give art its true meaning. Despite fears of job losses, AI is making workers more efficient, with companies retraining employees for higher-value tasks. The debate continues on whether AI scaling can persist through 2030, with technical and ethical challenges to overcome. Decision Intelligence is gaining traction as companies use data-driven insights to make smarter, real-time decisions across their operations. Emotion AI is on the rise, but its use raises concerns about privacy and bias, especially when applied to employee monitoring and decision-making. The music industry is wary of AI, with concerns about its impact on creativity, copyrights, and artist rights. Sam Bowman’s AI safety checklist outlines key areas needed to keep AI systems aligned with human values and prevent harmful misuse. The FBI cracked down on an elaborate AI-powered streaming-royalty heist, where musicians used AI tools to fraudulently inflate streaming numbers. The global distribution of AI compute infrastructure reveals an uneven landscape, with the U.S. and China leading, while Latin America and Southeast Asia lag behind. The debate over the definition of "open-source AI" is pushing for clearer guidelines to ensure transparency and ethical usage, particularly in critical sectors like healthcare and finance.

• AI is too 'sociopathic' to give financial advice, MIT researchers say - Hindustan Times MIT researchers state that AI models, like large language models, are currently unsuitable for providing financial advice due to their lack of understanding of human emotions and ethical considerations, leading to potentially harmful advice. To mitigate this, the researchers suggest incorporating stricter ethical guidelines, enhanced training on diverse and ethically sound datasets, and human oversight to ensure AI-generated advice aligns with ethical standards and user needs.?

• “Model collapse” threatens to kill progress on generative AIs examines "model collapse," a risk associated with training generative AI models primarily on synthetic data. When AI models learn from data generated by other AIs rather than from real-world data, they risk inheriting and amplifying errors, biases, and limitations, leading to a degradation in performance over time. As synthetic data becomes more prevalent for privacy and cost reasons, this issue poses a significant challenge. The article emphasizes the need for balanced data sources, combining synthetic and real-world data, to maintain AI model reliability and prevent performance decline.??

• And here is a solution for model collapse Research team proposes solution to AI's continual learning problem A team of researchers has developed a solution to improve the robustness of AI models against "model collapse," which occurs when AI models trained on data generated by other AIs degrade in performance over time. The team's approach involves creating a feedback loop that ensures AI models learn from both synthetic and real-world data. This method helps maintain model accuracy and reliability, preventing the accumulation of errors and biases that could lead to performance decline.??

• AI Has Created a Battle Over Web Crawling The IEEE Spectrum article explores how web crawling is used to enhance AI models by collecting vast amounts of data from the internet. The process involves automated scripts that systematically navigate and extract data from websites, which is then used to train AI models, especially in natural language processing and machine learning applications. Key steps include selecting target sites, extracting and cleaning text data, and structuring it for model training. While web crawling provides diverse and current datasets, it also presents challenges related to data quality, ethical concerns, and compliance with privacy regulations.?

This means that AI search engines can leverage richer, more diverse datasets, enhancing their ability to understand and respond to complex queries. This approach can improve the accuracy and relevance of search results by incorporating up-to-date web data. However, as companies like Perplexity plan to monetize their AI search capabilities, this could lead to a shift towards paid models for accessing advanced AI-driven search tools, balancing enhanced functionality with new revenue strategies.

• And more on AI-assisted search: AI-Assisted Search Will Change the Pay-To-Play Games for Content Marketers The article from the Content Marketing Institute discusses how AI-assisted search is transforming content marketing strategies. AI tools enhance search capabilities by delivering more relevant and personalized content recommendations based on user intent and behavior. This shift allows marketers to create more targeted content, improve user engagement, and drive conversions. As AI search evolves, marketers are encouraged to adapt their SEO and content strategies to leverage these advanced search capabilities effectively. This shift is already underway, with marketers encouraged to adapt their SEO and content strategies to maximize engagement and conversions. AI tools are increasingly being integrated into search engines, enabling smarter content recommendations and improving user experience.? Optimize for semantic search by using natural language and long-tail keywords. Ensure content is structured and accessible, incorporating AI-friendly formats like FAQs. Regularly update and diversify content to keep it relevant and engaging. Leverage AI tools to analyze search data and refine keyword strategies to match evolving user behavior and search engine algorithms.

• Why A.I. Isn’t Going to Make Art | The New Yorker argues that AI isn't poised to replace human artists because it lacks the consciousness, emotional depth, and cultural understanding necessary for genuine artistic creation. AI can generate images or mimic styles but doesn't experience life, emotions, or context—the elements that give art its profound meaning and connection to human experience. The piece emphasizes that while AI tools may enhance creativity by offering new techniques or ideas, true art comes from human insight and the complexities of the human condition that AI can't replicate.?

Yes, advertisers increasingly use AI to generate images for campaigns, utilizing AI's ability to quickly produce high-quality visuals in various styles. However, the article from The New Yorker argues that while AI can assist in creating images or mimic artistic styles, it does not possess the emotional depth, cultural understanding, or personal experience that defines true art. Advertisers use AI to enhance efficiency and creativity, but these AI-generated images are often seen as tools rather than genuine artistic expressions. What do we think?

• ?AI may not steal many jobs after all. It may just make workers more efficient - ABC News Alorica, a customer-service company, uses an AI translation tool to enable representatives to assist customers in 200 languages. Despite fears that AI could reduce jobs, Alorica and companies like IKEA are not cutting staff but retraining them for more complex tasks. AI is enhancing productivity, similar to past technological advances, by creating new roles rather than causing mass layoffs. The White House Council of Economic Advisers (report link: https://www.whitehouse.gov/wp-content/uploads/2024/07/Potential-Labor-Market-Impacts-of-Artificial-Intelligence-An-Empirical-Analysis-July-2024.pdf )? and studies from MIT and other institutions suggest AI will transform jobs rather than eliminate them.??

• Can AI Scaling Continue Through 2030? examines whether AI scaling can continue at its current pace through 2030. It highlights that AI development has experienced exponential growth due to advancements in computational power, data availability, and algorithms, particularly benefiting large language models (LLMs). The article notes that AI performance has improved predictably with larger models and bigger datasets, suggesting the potential to achieve or surpass human-level performance in many tasks by 2030 if these trends persist. However, it identifies several challenges, including the limits of current computational resources, the high energy consumption required for training large models, and the economic feasibility of maintaining such rapid scaling. The article proposes potential solutions, such as developing more efficient algorithms, advancing hardware technology, and optimizing data use. It also stresses the importance of policy and ethical considerations, given the potential societal impacts of advanced AI, like job displacement and privacy concerns. Overall, the article concludes that while AI scaling could theoretically continue, overcoming these technical, economic, and policy challenges is crucial to fully realizing AI's potential through sustained innovation and governance.?

• Why Decision Intelligence is reaching its stride, according to Aera CEO Fred Laluyaux Decision Intelligence is the practice of using data analytics, machine learning, and AI to enhance decision-making processes across organizations. It highlights several factors driving the adoption of Decision Intelligence. Firstly, businesses are facing an unprecedented volume of data, making traditional decision-making processes inefficient and less effective. DI offers a solution by integrating and analyzing vast amounts of data in real-time, providing actionable insights that can improve outcomes across various business functions, such as supply chain management, finance, and customer service. The increasing focus on automation and AI-driven decision-making reflects a broader shift towards more agile and data-driven business models.?

• Have you heard of emotion AI? 'Emotion AI' may be the next trend for business software, and that could be problematic | TechCrunch ? discusses the emerging trend of Emotion AI in business software and the potential issues it could raise. Emotion AI, also known as affective computing, involves technologies that can detect and interpret human emotions based on data such as facial expressions, voice tones, and physiological signals. This technology is being increasingly integrated into business software to enhance customer interactions, employee monitoring, and decision-making processes by adding a layer of emotional intelligence to digital communications. However, the article raises several concerns about the use of Emotion AI. One major issue is privacy: the collection and analysis of sensitive emotional data could infringe on individual privacy rights, especially if done without explicit consent. There are also questions about the accuracy and bias of Emotion AI systems, as the interpretation of emotions can be highly subjective and culturally specific, leading to potential misinterpretations and biased outcomes. The technology might reinforce stereotypes or make erroneous judgments based on incomplete or misleading emotional data.

The use of Emotion AI in the workplace, particularly for employee monitoring, could lead to a lack of trust and increased surveillance, potentially creating a hostile work environment. The article also touches on the ethical implications of Emotion AI in decision-making processes, where automated systems might make significant decisions based on inferred emotional states, which could be problematic or unfair.

So, while Emotion AI presents exciting possibilities for enhancing business software, its deployment needs to be approached with caution, ensuring ethical standards, transparency, and respect for privacy are maintained. As this technology evolves, companies and regulators will need to establish clear guidelines to address these challenges and ensure responsible use.?

• Grammy CEO says music industry also has AI concerns | TechCrunch discusses the concerns of the music industry regarding AI, as expressed by the Grammy CEO. The music industry is increasingly worried about the impact of AI on creativity, intellectual property, and artist rights. There are fears that AI-generated music could dilute the value of human-created art, infringe on copyrights, and create legal ambiguities around ownership and royalties. The Grammy CEO emphasizes the need for clear regulations and guidelines to protect artists' rights while balancing innovation. The industry seeks to ensure that AI technologies are used ethically and that the contributions of human artists remain respected and protected amidst the rise of AI-generated content.?

• The Checklist: What Succeeding at AI Safety Will Involve - Sam Bowman Sam Bowman’s article, "The Checklist: What Succeeding at AI Safety Will Involve," outlines the core areas required to ensure AI safety. The first focus is solving alignment challenges, meaning AI systems must follow human intentions reliably. Next is preventing AI misuse by creating mechanisms to safeguard against harmful applications. Finally, robust oversight is needed, particularly for powerful AI systems, to ensure responsible governance. The article stresses the importance of technical research and evaluating AI capabilities to avoid risks.?

• FBI busts musician’s elaborate AI-powered $10M streaming-royalty heist | Ars Technica The FBI has uncovered a $10 million streaming royalty scam, where musicians allegedly used AI-powered tools to inflate streaming numbers and collect fraudulent royalty payments. The scheme involved generating fake streams on platforms like Spotify and Apple Music, using AI to mimic human behaviors, and siphoning millions in royalties.?

• The Uneven Possibilities of Compute-based AI Governance Around the Globe The study investigates the global distribution of AI compute infrastructure and its implications for AI governance. It identifies three main categories: Compute North, which includes countries with advanced AI compute infrastructure for model training (e.g., the U.S. and China); Compute South, where compute infrastructure is more focused on deploying AI systems rather than training them (e.g., Latin American and some Southeast Asian countries); and Compute Desert, countries without any significant AI compute infrastructure.?

• Debate over “open source AI” term brings new push to formalize definition | Ars Technica The debate over "open-source AI" arises because there’s confusion and disagreement about what truly qualifies as open-source in the context of AI. Many AI models that claim to be open-source still rely on proprietary components or restrict certain uses, making them only partially open. Critics argue that calling these systems "open-source" misleads the public and developers, as full transparency and accessibility are key principles of open-source software. The debate is driven by concerns over transparency, accountability, and ethical usage, especially as AI systems play increasingly critical roles in sectors like healthcare and finance. There’s a push to establish clear definitions and standards to prevent misuse of the term and ensure that open-source AI aligns with its original principles.?

The push to formalize the definition of "open-source AI" is being led by a mix of stakeholders, including industry leaders, open-source advocates, AI researchers, and regulatory bodies. Organizations like the Open Source Initiative (OSI), which traditionally oversees open-source software definitions, may play a role in setting clearer standards for AI. Additionally, major AI companies and platforms such as Google, Anthropic, and Microsoft, as well as academic institutions and policy groups, are expected to contribute to this effort. Governments and international bodies are likely to get involved as well, especially as AI regulation becomes more crucial for ensuring ethical usage and accountability in critical sectors. This multi-stakeholder approach is intended to create a universally accepted framework for what qualifies as open-source AI, addressing both technical and ethical concerns.

?

News and updates around? finance, Cost and Investments

OpenAI is reportedly considering charging up to $2,000 a month for access to its new advanced language models, Strawberry and Orion, targeting high-end users with cutting-edge features. Nvidia saw a $279 billion market value drop as investors reassessed the tech rally, raising concerns about market overvaluation after the AI boom. Meanwhile, OpenAI co-founder Ilya Sutskever’s new AI startup, SSI, raised $1 billion to focus on making AI safer, highlighting the balance between innovation and responsible development. The AI investment surge is shaking up the venture capital market, as tech giants like Microsoft, Google, and Amazon pour billions into the sector, leaving traditional VCs struggling to compete. Broadcom and HPE have seen significant growth this quarter, thanks to strong demand for AI infrastructure and high-performance computing systems that support large-scale AI workloads. Finally, cloud revenues are expected to soar to $2 trillion by 2030, driven by the rapid adoption of AI, as businesses increasingly rely on scalable cloud platforms to manage AI applications and fuel innovation.

• Yowza! Report: OpenAI Considers $2,000 Monthly LLM Subscriptions OpenAI is reportedly considering subscription prices as high as $2,000 per month for its upcoming large language models, such as Strawberry and Orion. These advanced models will offer capabilities beyond current AI, including solving new math problems and performing deep research. The price reflects the models' expanded functionality, aimed at businesses and high-end users. OpenAI has yet to confirm official details but is exploring ways to make its offerings more attractive to investors while continuing to raise significant funding.??

• Nvidia suffers record $279 billion loss in market value as Wall St drops | Reuters On September 3, 2024, Nvidia's stock and related chip index tumbled as investors paused their support for the AI rally that had previously boosted tech markets. The dip followed a period of intense enthusiasm for AI and semiconductor stocks. Analysts believe the drop is due to concerns about market overvaluation, causing investors to reassess future growth. Nvidia, a key player in AI hardware, remains central to discussions on market trends.

• Then how do you explain this? Exclusive: OpenAI co-founder Sutskever's new safety-focused AI startup SSI raises $1 billion | Reuters Ilya Sutskever's new AI startup, SSI, which focuses on safety, raised $1 billion to ensure responsible AI development. The funds will be used to advance research in AI safety, mitigate risks related to AI deployment, and develop frameworks to ensure ethical AI applications. Sutskever’s goal is to balance innovation with safeguards, ensuring AI systems can be scaled while maintaining control over their potential impacts. This funding will help establish SSI as a leader in making AI safer for widespread use across industries.?

• AI craze is distorting VC market, as tech giants like Microsoft and Amazon pour in billions of dollars the current AI investment surge is being primarily funded by tech giants like Microsoft, Google, and Amazon. This trend is distorting the traditional venture capital (VC) model, as these corporations offer not just financial backing but also critical infrastructure, giving them a strategic advantage. Traditional VCs find it difficult to compete with the resources these giants provide, reshaping the landscape of AI funding and leaving smaller firms at a disadvantage. This could result in a shift where VCs focus on niche markets or early-stage startups while large corporations control the most promising AI innovations. The power dynamics of funding will continue evolving, potentially reshaping the broader tech investment landscape.?

• https://www.rcrwireless.com/20240906/network-infrastructure/ai-infrastructure-drives-broadcom-hpe-growth-in-the-quarter highlights how investments in AI infrastructure have fueled significant growth for Broadcom and Hewlett Packard Enterprise (HPE) during the recent quarter. Both companies benefited from increasing demand for AI-driven data centers and networking solutions. Broadcom’s revenue surged due to its strong position in AI chips and semiconductors, which are essential for building advanced AI systems. Similarly, HPE saw growth in its AI infrastructure offerings, particularly its high-performance computing (HPC) systems designed to support large-scale AI workloads.?

• Cloud revenues poised to reach $2 trillion by 2030 amid AI rollout | Goldman Sachs ? projects that cloud revenues could reach $2 trillion by 2030, driven by the rapid adoption of AI technologies. The rollout of AI is significantly boosting demand for cloud infrastructure, as businesses across industries increasingly rely on cloud-based services to manage, store, and process data for AI applications. The report emphasizes that AI workloads require scalable cloud platforms, which are propelling growth for cloud providers. The combination of AI and cloud computing is expected to create new business models, enhance operational efficiencies, and fuel innovation, making cloud infrastructure a critical component of future enterprise growth.??

What/where/how Gen AI solutions are being implemented today?

The UK government is rolling out AI training courses for 5,000 civil servants by 2025, aiming to boost public sector efficiency with skills in machine learning and natural language processing. In Jefferson County, Colorado, the 9-1-1 center is using AI to help prioritize emergency calls during a staffing shortage, automating parts of the response process to improve service levels. While AI is aiding emergency services, concerns remain about safety and reliability in high-stakes situations, where human oversight is still crucial. A UK school has introduced the country's first teacherless classroom, using AI to manage lesson plans and provide personalized feedback, but questions linger about its ability to handle discipline and offer the human interaction that students might still need.

• UK government introduces AI training courses for civil servants to boost public sector efficiency The UK government has launched AI training courses for civil servants to enhance public sector efficiency, aiming to train 5,000 staff by 2025. These courses focus on using tools like natural language processing (NLP) and machine learning to automate routine tasks, analyze large datasets, and improve decision-making. The initiative aims to equip civil servants with practical AI skills, covering AI fundamentals, ethical considerations, and practical applications to ensure the responsible integration of AI technologies in government operations.?

• Jefferson County, CO, 9-1-1 Center Uses Artificial Intelligence During Staff Shortage | Firehouse Jefferson County's 9-1-1 center in Colorado is using artificial intelligence to address staffing shortages. The AI technology assists with call triage, helping operators prioritize emergency responses more efficiently. By automating some of the decision-making processes, the center can maintain effective service levels despite fewer human operators. This innovation aims to enhance response times and accuracy while reducing the workload on the remaining staff.

Using AI in Jefferson County's 9-1-1 center raises concerns about safety and reliability, especially in critical situations. While AI can help prioritize calls and reduce operator workload, there is a risk if the AI misinterprets information or lacks the nuanced understanding of a human operator. Ensuring robust oversight and proper integration with human operators is crucial to maintaining safety and effectiveness in emergency response situations.

• ?School introduces UK's first 'teacherless' classroom using artificial intelligence A UK school has introduced the country's first teacherless classroom using artificial intelligence. The AI system manages lesson plans, monitors student progress, and provides personalized feedback, aiming to enhance learning efficiency and reduce the need for human teachers. This innovative approach is designed to supplement traditional teaching methods, offering a glimpse into the potential future of education.

The AI-driven, teacherless classroom in the UK raises questions about safety and effectiveness, particularly in handling discipline and personalized learning. While AI can provide tailored educational content and monitor student progress, there are concerns about its ability to manage behavioral issues and maintain classroom order. Some students appreciate the novelty and personalized feedback, while others may miss human interaction and guidance. The AI system uses data analytics to identify disruptive behaviors, but human oversight remains crucial to address these challenges effectively. And God forbid if there is an emergency …

?

Women Leading in AI?

New Podcat Check out our latest Women And AI podcast episode Cindy Lin from Bundles, discussing AI's transformative role in finance. The conversation covers how AI revolutionizes algorithmic trading and equity research, while addressing misconceptions like guaranteed high profits and the difficulty of implementation. Key challenges include data quality, model bias, and regulatory compliance. The post invites listeners to explore game-changing AI tools, emphasizing the need for collaboration, and prompts readers to share their thoughts on AI managing their investments.

Featured AI Leader ??Women And AI’s Featured Leader -Thais Fernandes ???

Thais uses AI to enhance brainstorming, automate tasks, and streamline her workflow, with ChatGPT as her favorite tool. She advises beginners to focus on solving practical problems for more effective learning.

Learning Center and How To’s

Microsoft researchers have developed a clever hybrid technique that combines small and large language models to detect hallucinations faster and more accurately, balancing speed and precision. The Command R Series models are built to integrate smoothly into existing workflows, helping businesses fine-tune AI for tasks like customer support and content moderation with ease. Anthropic's GitHub quickstart projects make it easier for developers to build AI applications using Claude, offering a range of customizable solutions for customer support and more. Knowledge Graphs are now boosting Retrieval-Augmented Generation (RAG) systems by connecting related documents more effectively, reducing hallucinations and improving accuracy in areas like legal and technical documentation. Matt Shumer’s Reflection Llama 3.1-70B on Hugging Face is a powerful 70-billion parameter language model designed for complex natural language processing tasks. HackerNoon’s guide to fixing AI hallucinations offers practical steps for improving the accuracy of RAG systems, from better data retrieval to supervised learning and ongoing feedback loops.

• Building LLMs from the Ground Up: A 3-hour Coding Workshop - YouTube https://www.youtube.com/watch?v=quh7z1q7-uc ?

• Microsoft Researchers Combine Small and Large Language Models for Faster, More Accurate Hallucination Detection - MarkTechPost Microsoft researchers have developed a technique to improve hallucination detection in AI models by combining small and large language models (LLMs). The approach involves using a smaller, faster model to generate initial responses, which are then cross-checked by a larger, more accurate model for hallucination errors. The process consists of several steps: the small model first identifies potential hallucinations quickly, and then the larger model verifies these findings, providing a more comprehensive analysis. The technique uses a smaller, faster model to quickly generate and filter initial responses for potential hallucinations. These flagged responses are then cross-verified by a larger, more computationally intensive model that provides a deeper analysis. This two-step process leverages the strengths of both models—speed and accuracy—resulting in faster, more reliable hallucination detection. The hybrid approach balances computational efficiency and model performance, enhancing the robustness of AI systems against generating misleading information.?

• Updates to the Command R Series the Command series models are designed to be easily integrated into existing workflows, allowing businesses to leverage advanced AI capabilities without requiring extensive technical expertise. The series also focuses on fine-tuning models for specific use cases, like customer support and content moderation, enabling more accurate and relevant responses tailored to the needs of different industries. These models are designed to handle a broad range of tasks, from generating creative content to providing detailed data analysis, and are optimized for understanding nuanced language inputs.?

• GitHub - anthropics/anthropic-quickstarts: A collection of projects designed to help developers quickly get started with building deployable applications using the Anthropic API ? provides developers with projects designed to build deployable applications using the Anthropic API. Key resources include a Customer Support Agent powered by Claude for AI-assisted support, and setup instructions for each project. Developers can easily customize these projects, requiring only an API key and following provided instructions to install dependencies and run applications. The repository promotes contributions and offers community support via Discord.?

• Boost LLM Results: When to Use Knowledge Graph RAG - The New Stack explains (with examples) that Knowledge Graphs can enhance Retrieval-Augmented Generation (RAG) by explicitly connecting documents that share meaningful relationships, such as HTML links, keywords, or defined terms. In standard RAG, vector retrieval identifies documents based on similarity, but it may miss related information spread across multiple sources. A knowledge graph allows the RAG system to "traverse" these relationships, ensuring deeper, more accurate retrieval of relevant documents, reducing the risk of hallucinations and improving response quality in complex domains like legal or technical documentation.?

• Building LLMs from the Ground Up: A 3-hour Coding Workshop ? Enjoy!

• mattshumer/Reflection-Llama-3.1-70B · Hugging Face The Reflection Llama 3.1-70B model on Hugging Face is a 70-billion parameter large language model developed by Matt Shumer. This model focuses on improving AI’s ability to generate and understand complex language tasks, leveraging advanced training techniques to enhance performance in natural language processing tasks. It is designed for developers and researchers seeking high-quality language models for applications requiring nuanced and context-aware responses.??

• Say Goodbye to AI Hallucinations: A Simple Method to Improving the Accuracy of Your RAG System | HackerNoon ddresses AI hallucinations by offering a method to improve the accuracy of Retrieval-Augmented Generation (RAG) systems. It outlines several steps to enhance the reliability of AI-generated outputs. First, it recommends optimizing the data retrieval process by integrating a reliable and comprehensive knowledge database, ensuring the AI pulls relevant, accurate information. Next, the article emphasizes fine-tuning the generation process through supervised learning, training the model to reduce hallucinations based on past mistakes. Additionally, it highlights the importance of implementing a feedback loop, where human reviewers assess AI outputs, providing corrections that allow the system to learn from its errors. This continuous feedback strengthens the model over time. Lastly, the article stresses the need for regular updates to both the retrieval system and the model itself, keeping the AI system current and further reducing inaccuracies. By following these steps, organizations can significantly minimize AI hallucinations and increase the overall trustworthiness of AI systems.??

Prompt of the week

Library - Anthropic collection of prompts. This library includes a collection of example prompts tailored for various use cases, such as customer support, creative writing, coding assistance, and educational purposes. Each prompt is carefully crafted to demonstrate how specific instructions can yield more accurate and relevant responses from AI, showcasing the importance of clear and concise communication when using AI tools.

The library is structured to help users understand different prompting techniques, such as using context, setting explicit instructions, and defining the format of the desired output. It also highlights best practices for prompt design, including how to handle ambiguous queries, refine prompts based on initial outputs, and use iterative approaches to improve AI responses. It also? provides examples of multi-step prompts for more complex tasks, illustrating how users can build upon AI responses to achieve more detailed or nuanced outcomes.

Here are some trainings and templates from Antropic for the engineering prompts that you can create yourself.?

• Google Sheets tutorial on prompting from Anthropic: Anthropic's Prompt Engineering Interactive Tutorial [PUBLIC ACCESS]
• ?GitHub Antrhopic prompt training: Anthropic's Prompt Engineering Interactive Tutorial ?
• Automatically generate first draft prompt templates - Anthropic ?
• Prompt engineering overview - Anthropic
• Automatically generate first draft prompt templates - Anthropic ??

Tools and Resources

Job hunting with AI is becoming more popular, with tools that help optimize resumes for ATS, provide personalized job recommendations, and even simulate interview scenarios, but their success still depends on user input and real-life nuances. Networking AI tools make connecting with employers more efficient, but the key to success lies in building genuine relationships beyond automated outreach. Meanwhile, Harvey.AI ’s BigLaw Bench is revolutionizing how large law firms assess AI legal tools by offering detailed performance metrics, helping them make smarter decisions about integrating AI into their operations.

• Job Hunting With AI: 4 Techniques We've Tried and How They Worked Out - CNET The first technique involves using AI-powered resume builders, which help optimize resumes by suggesting relevant keywords, formatting, and content based on job descriptions. These tools are generally effective in improving resume visibility in Applicant Tracking Systems (ATS), although they may not always account for nuances specific to certain industries or roles. The second technique is leveraging AI for personalized job recommendations. These platforms analyze user profiles and past job search behavior to suggest jobs that align with the user's skills and preferences. While these tools can offer tailored suggestions, the article notes that the quality of recommendations heavily depends on the quality of data input by the user and the platform's algorithm's sophistication. The third method discussed is utilizing AI-driven interview preparation tools, which simulate interview scenarios and provide feedback on user responses. These tools can be particularly beneficial for practicing responses to common questions and improving interview performance. However, they might not fully replicate the nuances of a real-life interview, such as spontaneous questions or the interviewer's unique style. Lastly, the article examines AI tools designed for networking and relationship management, which help job seekers identify and connect with potential employers or industry professionals. These tools can automate outreach and suggest personalized messages, making networking more efficient. However, the effectiveness of these tools is contingent on the user's ability to build authentic relationships beyond the initial AI-generated outreach.

• https://www.harvey.ai/blog/introducing-biglaw-bench introduces BigLaw Bench, a groundbreaking tool developed by Harvey.AI to assist large law firms in assessing the performance of AI legal tools. BigLaw Bench provides a comprehensive benchmarking system that evaluates various AI legal applications based on their effectiveness in performing key legal tasks such as document review, contract analysis, and legal research. The tool aims to help law firms understand which AI solutions best meet their needs by offering detailed insights into performance metrics, accuracy, and overall efficiency. By offering a clear comparison, BigLaw Bench helps firms optimize their AI adoption strategy, enabling them to make more informed decisions about integrating AI into their operations.

If you enjoyed this newsletter, please comment and share. If you would like to discuss a partnership, or invite me to speak at your company or event, please DM me.