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- Apple GPT to rival ChatGPT?
☕️ Good morning. It's Thursday, July 20 I've got some awesome updates to share with you today. Brace yourself for the coolest AI trends, valuable insights, resources, and mind-blowing cutting-edge tools. Get ready to be amazed! First time reading? join here!
Apple GPT to rival OpenAI’s ChatGPT? Apple, traditionally cautious about AI, has secretly developed its generative AI tools and a chatbot, internally referred to as "Apple GPT." This news briefly boosted its stock. The company is yet to finalize plans for a consumer release. Experts believe Apple's strong brand and hardware-software integration gives it an advantage in the AI space. However, Apple remains cautious about AI development, citing various issues.
🗞️ Today’s Highlights:
EXECUTIVE — Should you build or buy Generative AI?
IDEAS & QUESTIONS — Generative AI: hype, or truly transformative?
LEARNING — Llama 2 is here - get it on Hugging Face
AI TOOLS — Visual QR: Generate beautiful QR codes using AI
USE CASES — How Generative AI Will Shape the Face of the Beauty Industry
WORK — Generative AI meets software development: the advent of generative coding
🗞️ EXECUTIVE
Apple has reportedly been secretly developing its own generative AI tools, while other companies compete in the public space. According to Bloomberg, the Cupertino-based company has created its own framework called "Ajax" to build large language models, similar to systems like ChatGPT and Bard. Additionally, Apple is said to have developed its own chatbot, internally referred to as "Apple GPT." The news of these developments led to a 2.3% increase in Apple's stock, reaching a record high of $198.23 and adding $71 billion to the company's market cap, which stood at $3 trillion. However, there is still no finalized plan for releasing Apple's generative AI offering to consumers. The AI initiative has become a significant focus within Apple, involving multiple teams. Apple representatives have not yet commented on the report.
Generative AI adoption is essential for organizations. The decision to build or buy models is complex. Options include taking AI through APIs or applications, shaping existing models with fine-tuning, or making custom models. Buying is preferred by smaller organizations. Benchmarking is important. Data privacy and governance must be considered. Verify outputs and clean data. Treat generative AI as a product. CIOs should understand its limitations.
Google Cloud announced the general availability of four key foundation models for Vertex AI: Imagen, PaLM 2 for Chat, Codey, and Chirp. These models offer advanced features such as image generation, editing, captioning, VQA, chat enhancement, code generation and completion, and multilingual Speech AI. They provide powerful tools for various applications.
With both a wide selection of foundation models and extensive, enterprise-grade platform capabilities, Vertex AI continues to unlock ways for your business or organization to access foundation models, tune them on your proprietary data, and leverage them for differentiated apps and digital experiences. To take the next step, visit our product page.
CEOs face a defining moment in adopting generative AI. The right investments can bring strategic advantages, but wrong decisions may lead to privacy concerns, legal liabilities, and ethical issues. Despite pressure to act fast, 60% of organizations lack a consistent approach to generative AI. CEOs need to align goals with strategy, assess risks, and promote cross-functional understanding. Balancing risk and reward is crucial, and the IBM Institute for Business Value offers research-backed guides to help CEOs manage the transformative impact of generative AI. Here is the guide.
Iain Brown, Head of Data Science for SAS, Northern Europe, discusses the future of generative AI, highlighting its potential, concerns around bias, and the need for responsible development. Generative AI, driven by technologies like GANs and LLMs, has transformed sectors and changed human interactions with technology. Text-based and visual AI models show promise in content creation and image generation, but there are risks of fraud and bias. The Metaverse and audio industry can benefit from generative AI, but privacy, inclusivity, and consent must be prioritized. The responsible use of generative AI requires transparency, accountability, and protection of intellectual property. Fine-tuning models and striking a balance between technological advancements and trustworthiness is key to harnessing the potential of generative AI responsibly.
🗞️ IDEAS & QUESTIONS
In a Goldman Sachs Exchanges episode, Sarah Guo, Gary Marcus, Kash Rangan, and Eric Sheridan discuss the surge in investor interest in generative AI technology. They question whether the hype and market pricing around the technology are justified and explore its disruptive potential.
🗞️ LEARNING
Meta has unveiled Llama 2, a new generation of open-access large language models. It has gained significant attention for its advanced capabilities and is being fully integrated into the Hugging Face platform. Llama 2 comes with a community license that allows extensive usage, including commercial purposes. Meta has released the code, pre-trained models, and fine-tuned models, making them accessible to users starting today.
The Llama 2 release introduces a new set of pre-trained and fine-tuned language models called LLMs. These models vary in size from 7 billion to 70 billion parameters (7B, 13B, 70B). Compared to the previous Llama 1 models, the pre-trained models in Llama 2 offer several significant improvements. They are trained on 40% more tokens, have a longer context length of 4,000 tokens, and utilize grouped-query attention to enable faster inference for the 70B model.
The most exciting aspect of this release is the introduction of the fine-tuned models, specifically Llama 2-Chat, which have been optimized for dialogue applications using Reinforcement Learning from Human Feedback (RLHF). These models outperform most open models and achieve similar performance to ChatGPT, a widely recognized language model, based on evaluations conducted by humans. The fine-tuned models in Llama 2-Chat have undergone extensive training to enhance their helpfulness and safety, as demonstrated by various benchmarks.
🗞️ AI TOOLS
Broadcast: Stop wasting time manually writing weekly updates. Quickly draft updates in Broadcast with AI and share on Slack and Email. Broadcast makes sending weekly updates fast, and simple.
Visual QR: Generate beautiful QR codes using AI.
Kickresume: Create a beautiful resume quickly with the help of artificial intelligence and our customizable templates. Impress your future employer with a perfect resume created in minutes.
Memorable: High-accuracy AI to improve branding and the performance of every ad
Wope: New Era of Rank Tracking. Find out what's working and what's not to get more search traffic. Like an SEO consultant who can analyze millions of data.
Disclaimer: 1) The tool descriptions may include messaging from each tool site. 2) Please thoroughly read the site details before using and/or acquiring any of the tools listed above.
🗞️ USE CASES
Roughly 10 billion global search queries are made daily, with half of them remaining unanswered, mostly in the shopping category. Microsoft's Divya Kumar believes generative AI can streamline the shopping process by connecting consumers with brands. Samyutha Reddy sees generative AI impacting marketing and preserving brand identity. Haute.AI uses generative AI for personalized product recommendations. Educating about the value of generative AI and overcoming challenges is crucial. Reddy envisions generative AI lowering barriers to entry for founders and creators. Executives should adopt a few AI tools to enhance marketing effectiveness.
The history of AI in the stock market began with the rise of algorithmic trading, which involved using computer algorithms trained to identify patterns from past data to automate trading. This innovation greatly improved efficiency and reduced human error in trading. Generative AI is poised to push the boundaries further in this field.
According to a recent report by McKinsey, the retail industry has the potential to benefit greatly from generative AI, with an estimated additional value of around $310 billion. This value can be achieved by leveraging AI to improve various key functions such as marketing and customer interactions, thereby transforming the retail landscape. The impact of AI goes beyond changing business operations; it will also revolutionize the way consumers interact with brands. Retailers are projected to harness these advancements to develop more efficient marketing strategies, personalize customer interactions based on individual preferences, and ultimately boost revenue growth. The integration of AI in the retail sector will introduce a new dynamic between retailers and consumers, fostering an interactive and engaging shopping experience.
🗞️ WORK
The labor market is expected to undergo significant transformations in the coming years. Projections suggest that about 23% of current jobs will experience changes by 2027, resulting in both new job opportunities and job losses. This transition is projected to create around 69 million new jobs, but it will also lead to the loss of approximately 83 million jobs over the next five years.
Automation is identified as a key factor in these changes, with the potential to revolutionize various sectors. Office and administrative support tasks are considered the most susceptible to automation, with a staggering 46% automation potential. Legal tasks closely follow, with a potential automation rate of 44%. However, industries such as construction, building, and cleaning tasks have lower possibilities for automation, averaging around 25%.
These statistics provide valuable insights into the evolving dynamics of the labor market. They highlight the importance of individuals and organizations adapting to this changing landscape in order to stay relevant and competitive.
HFS Research predicts the rise of generative coding, which automates the creation, manipulation, and optimization of code. It combines human and machine coding practices throughout the software development lifecycle. The convergence of internet, data, and AI technologies is driving significant changes in software development. Generative coding frees developers from low-value tasks, allowing them to focus on more abstract work. The future of software development will involve teams of humans and machines working together. HFS recommends industry certification for generative AI models and encourages organizations to prepare for this shift in software development.
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