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#ai #genai trends 1. Generative-native AI companies continue to see high growth, particularly in enterprise productivity. 2. Gen-enhanced applications persist in their ability to deliver value-add AI capabilities for their customers. 3. Companies are exploring new autonomous capabilities of AI to take on tasks. 4. AI companies continue to secure funding and high valuations at a rapid clip 5. Intelligent applications have an increased focus on driving ROI 6. The relationship between AI and when or how humans are “in the loop” is shifting, particularly in historically services-oriented workflows 7. Enablers are transforming the data landscape by changing how companies process and leverage unstructured and structured data 8. Leaps by open-source models and infrastructure companies are driving both value and speed https://lnkd.in/gt-fb3hX?

AI’s big shift from ‘model-forward’ innovation to ‘product-forward’ U.S. Federal Trade Commission (FTC) launches an inquiry into three groundbreaking generative AI partnerships: Microsoft and OpenAI, Google and Anthropic, and Amazon and Anthropic. The investigation seeks insights into the agreements' specifics, practical implications, competitive impact, and the competition for AI inputs. The outcome could reshape the landscape for these tech giants and have significant implications for the broader AI and technology sector. The results show a transformative shift in the AI industry — away from building models from scratch towards leveraging existing models through APIs. According to Menlo Ventures' survey, almost 95% of AI spending is now directed at inference rather than training, signaling a move towards a "product-forward" approach. This shift poses challenges and opportunities for companies, emphasizing the importance of prompt engineering, proprietary data, and creative ways to use generative AI models. As companies increasingly abandon building AI models from the ground up to utilize existing ones, the focus has shifted towards prompt engineering and incorporating proprietary data. The rise of APIs, with a projected total value of $5.4 trillion by 2027, has democratized access to advanced AI. Still, it also raises questions about maintaining a competitive edge when everyone is using similar models. While the current spotlight is on techniques like retrieval-augmented generation (RAG), the industry is already contemplating the next steps in evolving these models to create increasingly sophisticated applications, ensuring continued differentiation in the AI market. Read more: https://lnkd.in/ed2i-9VQ #ai #ventures #product #business #generativeai #ftcinvestigation #api #apishift #promptengineering #aiindustryshift #competitiveadvantage #ragtechnique #proprietarydata #aiapplications

In case you missed it, Adaptive ML launching out of stealth today to build a Reinforcement Learning from Human Feedback (RLHF) platform for enterprises helping personalize (in a scalable way) AI. Great to be working with Adaptive and Index on this one.

Are you fascinated by the potential of generative AI platforms? This blog dives into the world of LLMOps, exploring how it merges AI and operations to revolutionize various industries. 𝐋𝐋𝐌𝐎𝐩𝐬 𝐮𝐧𝐥𝐨𝐜𝐤𝐬 𝐚 𝐭𝐫𝐞𝐚𝐬𝐮𝐫𝐞 𝐭𝐫𝐨𝐯𝐞 𝐨𝐟 𝐨𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐢𝐞𝐬: > Enhanced creativity and innovation through concept exploration and unconventional problem-solving. > Streamlined operations and automation for tasks and processes, leading to increased efficiency. > Personalized user experiences with custom interfaces and content tailored to individual preferences. The blog also explores the real-world applications of generative AI platforms in content creation, customer support, predictive analytics, and more! Don't miss out, check out the full article here! - https://lnkd.in/gzFCQ66V #LLMOps #GenerativeAI #ArtificialIntelligence #MachineLearning #Innovation #Technology #FutureofWork #goML

Is Generative AI a Bubble or a Tree? The debate over the sustainability of generative AI heats up. OpenAI, for instance, faces enormous operational costs, with projected losses up to $5 billion in 2024. Despite this, AI influencers and investors like Kai-Fu Lee remain optimistic, predicting that OpenAI could become a trillion-dollar company in the near future. On the other hand, financial institutions and analysts express concerns about the high costs and profitability of generative AI. Companies like Microsoft and Google continue to invest heavily in AI infrastructure, even as growth slows, indicating that the industry's payoff might take longer than expected. Critics argue that some of the current hype around AI might lead to a bubble, with Gartner predicting a 30% abandonment rate for generative AI projects by 2025. However, proponents see AI as a transformative technology, akin to a tree growing over time rather than a fleeting bubble. As we navigate this evolving landscape, the key will be balancing hype with sustainable innovation and acknowledging that not all AI ventures will succeed. Generative AI's future could be defined by its ability to solve complex problems and adapt to changing needs. https://lnkd.in/g6xA4ztV

Already there are many writings online discussing the potential of Gen AI in productivity, creativity and new discoveries. Massive investment thru VC to Gen AI startups and large corporations ramping up spending on this technology in the hope of automate their workflows / outputs. I think this article "Who Profits the Most from Generative AI?" by MIT SMR, is a wonderful piece to understand the Gen AI Industry value chain. ie How every pieces stack-up to deliver an LLM application to companies & users. 1️⃣ Computing Infrastructures, where leading by NVIDIA, AMD, TSMC, Intel, etc 2️⃣ Data, technically almost the entire public internet, ebooks. 3️⃣ Foundation Model, leading by OpenAI, Anthropic, Google, Meta , where size-matters and founding capabilities. 4️⃣ RAGs and Fine-tuned models, which represent the service that aims to use an LLM for a specific purpose, to perform a context-specific tasks, with domain-specific data. 5️⃣ LLM Applications, like GitHub Copilot (coding), Sudowrite (creative writing), Alltius (customer support), etc... task-oriented offering. The authors made a few "predictions".... ▶ The reasons that most cloud startup failed or were bought by rivals apply to generative AI as well --> Because continuous investment (competing resources & supply), Model size dictate LLM performance and economic-of-scale. ▶ Organisations that have access to lots of high-quality data in specific domains might have an advantage in creating specialised models. --> Strong Incumbents advantages, could be find in financial institutions, insurance, media and health-care, etc. e.g. Bloomberg ▶ Concerns around training models on copyrighted content are likely to result in a greater advantage for established players in generative AI. --> resources to battle copyright claims in court .... again $$$ matters. For fellow transformaton leaders, keep an eye on 4️⃣ , 5️⃣ , where industry-specific & task-specific Gen AI may be an answer. Also, if your company already have unique data and domain-knowledge, that enable and govern a fune-tuning or RAG, that may ultimately create an advantage. (but beware of leak of proprietary knowledge!!!). There are other types of AI, may already promising in performance and less running cost & less energy consumption than Gen AI !! Be considerate on the choice. Cheers. 😉 #AI #GenAI https://lnkd.in/e9qv46AB

New Post: WitnessAI is building guardrails for generative AI models - https://lnkd.in/g_7pA2HY - Generative AI makes stuff up. It can be biased. Sometimes, it spits out toxic text. So can it be “safe”? Rick Caccia, the CEO of WitnessAI, believes it can. “Securing AI models is a real problem, and it’s one that’s especially shiny for AI researchers, but it’s different from securing use,” Caccia, formerly SVP of © 2024 TechCrunch. All rights reserved. For personal use only. - #news #business #world -------------------------------------------------- Download: Stupid Simple CMS - https://lnkd.in/g4y9XFgR -------------------------------------------------- or download at SourceForge - https://lnkd.in/gNqB7dnp

Great market study of Generative AI. The key highlights to me were: - Almost all enterprise AI usage is OpenAI today - Looking at new investments, OpenAI is already in the minority - Open source and control are huge

Google’s #AI journey serves as a cautionary tale for industry leaders on the importance of agility and innovation in maintaining market dominance. Here is four learning points from Google's AI adventure: 🔗 https://lnkd.in/gH6eGWxx Tor Wallin Andreassen | Yngvar Ugland | Norges Handelshøyskole (NHH) | #ai #agility

Generative AI has swiftly revolutionized various sectors, spanning entertainment, healthcare, finance, and marketing, by autonomously creating realistic content, encompassing images, videos, and text. In 2023, a Statista survey unveiled that 29% of Gen Z, 28% of Gen X, and 27% of millennials in the US embraced generative AI tools. Noteworthy industries like technology, education, business services, manufacturing, and finance exhibit a strong preference for OpenAI's solutions. Gartner forecasts that by 2025, generative AI will generate 10% of all data, a remarkable leap from its current sub-1% share. Furthermore, the generative AI market, which was valued at USD 7.9 billion in 2021, is anticipated to soar to USD 110.8 billion by 2030, with the Asia-Pacific region displaying remarkable growth. Unicorn companies like OpenAI and Hugging Face are at the forefront of this investment wave, as generative AI companies have secured $1.7 billion in VC funding over 255 deals in recent years. This dynamic landscape underscores Generative AI's pivotal role in reshaping industries and charting the course for our future. However, the full potential of Generative AI lies in its operationalization, particularly at scale. It's not merely about having these powerful AI models; it's about seamlessly integrating them into business operations. This integration unlocks a multitude of practical benefits that span across industries, offering a roadmap to streamline processes, boost efficiency, and unlock substantial time and resource savings. #generativeai #mlops #fmops #llmops #genops