Do you know Artificial General intelligence(AGI) the ability of a machine to carry out any intellectual work that a human can? OpenAI has unveiled a five-level framework to monitor its advancement in this direction. Level 1: Chatbots: AI systems are now capable of having conversations with people. The majority of AI in use today, such as ChatGPT from OpenAI and other chatbots like Claude, are in this stage. These systems are helpful for customer service, virtual support, and other applications needing human-like interaction. Level 2: Reasoners: AI systems that can handle issues on a human level are represented by Level 2 "Reasoners". The next milestone is to achieve expert-level reasoning, but current models such as ChatGPT can display reasoning capabilities on occasion. Level 3: Agents :This level of AI allows it to act on behalf of users. This involves carrying out activities, coming to conclusions, and carrying out plans on your own. Agentic AI is being developed by a number of startups, including Devin, the first fully autonomous AI software engineer in history. Level 4: Innovators :The level four AI systems are capable of inventing new things. This includes coming up with unique concepts and inventions in addition to problem-solving techniques. Such AI could revolutionize sectors like science, technology, and engineering by pushing the boundaries of human knowledge and creativity. Level 5: Coordinators :Level 5, where AI can carry out the tasks of large organizations, is the aim of AGI. This degree of AI would be able to oversee intricate procedures, make critical choices, and plan extensive activities. Achieving this level would represent the realization of AGI, where AI surpasses human capabilities in the most economically valuable tasks. According to Bloomberg, OpenAI is close to attaining Level 2. This advancement is critical because it will improve the precision and reliability of AI systems, increasing their utility across a range of industries. OpenAI’s five-level structure provides a clear roadmap for tracking AI progress. It provides a methodical approach to gauging progress and comprehending each step towards artificial intelligence. These stages will aid in directing the creation of ever-more complex systems as AI develops. What do you think about the Open AI 5 Level structure and what you feel can transform the Industry of Tech?
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Executive Director, FCC Tech Advisory | Strategic Advisor for Data and Technology | Talks about Innovation | Ex-HSBC
Problem with the first wave of GenAI was that it was driven by CEO community looking for optical edge and buying licenses like a hot cake. The slump is a good sign that the CEO optimism is giving way for a more use case driven approach. Two things can happen. 1. teams on the ground come up with user stories around existing processes 2. Visionary leaders rethink the processes using AI Both are welcome.
The honeymoon phase of generative AI is over A great research report by LucidWorks that reflects what I am also seeing: - Generative AI projects are not going well - Cost and Security is a growing concern - Many companies are not seeing tangible benefits from Generative AI - There is a long path from using a LLM to delivering value in an organization The survey insights are: - Conducted among 1,000 companies across 14 industries. - 63% plan to increase AI spending in 2024, down from 93% in 2023. - 36% will maintain current AI spending levels, up from 6% last year. Challenges noted: - Financial returns on AI projects have been underwhelming. - Only 25% of planned AI investments have been fully implemented. - Significant increase in AI project costs and accuracy concerns. h/t Alastair Roriston Cost and model preferences: - 49% use commercial LLMs like Google's Gemini and OpenAI's ChatGPT. - 30% use a mix of commercial and open-source models. - Shift towards open-source models expected due to performance gains and cost benefits. I won't give away all the great points, but it's a free useful data point for everyone. https://lnkd.in/gBaAtCSm
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Neural network models are fundamentally a black box, making it tough to evaluate a model’s behavior. Many GPT wrappers built these days though prompt engineering and fine tuning only have the end in mind, which is what they wish would happen in an ideal operating scenario. But often it is hard to think of all the permutations of possible action and outcomes of an end user, which might ultimately expose some security loopholes. How do you safely navigate around using your models in production?
The honeymoon phase of generative AI is over A great research report by LucidWorks that reflects what I am also seeing: - Generative AI projects are not going well - Cost and Security is a growing concern - Many companies are not seeing tangible benefits from Generative AI - There is a long path from using a LLM to delivering value in an organization The survey insights are: - Conducted among 1,000 companies across 14 industries. - 63% plan to increase AI spending in 2024, down from 93% in 2023. - 36% will maintain current AI spending levels, up from 6% last year. Challenges noted: - Financial returns on AI projects have been underwhelming. - Only 25% of planned AI investments have been fully implemented. - Significant increase in AI project costs and accuracy concerns. h/t Alastair Roriston Cost and model preferences: - 49% use commercial LLMs like Google's Gemini and OpenAI's ChatGPT. - 30% use a mix of commercial and open-source models. - Shift towards open-source models expected due to performance gains and cost benefits. I won't give away all the great points, but it's a free useful data point for everyone. https://lnkd.in/gBaAtCSm
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This is the case with most projects though. Only fraction is implemented after MVP but those can generate a large returns. Important is to recognise quickly the bad ones so that they don’t diminish the returns of good ones. The process for failing fast is more important than sucess of any individual project.
The honeymoon phase of generative AI is over A great research report by LucidWorks that reflects what I am also seeing: - Generative AI projects are not going well - Cost and Security is a growing concern - Many companies are not seeing tangible benefits from Generative AI - There is a long path from using a LLM to delivering value in an organization The survey insights are: - Conducted among 1,000 companies across 14 industries. - 63% plan to increase AI spending in 2024, down from 93% in 2023. - 36% will maintain current AI spending levels, up from 6% last year. Challenges noted: - Financial returns on AI projects have been underwhelming. - Only 25% of planned AI investments have been fully implemented. - Significant increase in AI project costs and accuracy concerns. h/t Alastair Roriston Cost and model preferences: - 49% use commercial LLMs like Google's Gemini and OpenAI's ChatGPT. - 30% use a mix of commercial and open-source models. - Shift towards open-source models expected due to performance gains and cost benefits. I won't give away all the great points, but it's a free useful data point for everyone. https://lnkd.in/gBaAtCSm
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Computerworld Columnist, writer for CSO Online, CIO.com, podcaster, blogger. Focuses on cybersecurity and analytics issues for IT and Security enterprise executives.
The results here are clearly not surprising, but it does force the question of whether companies are failing GenAI or is GenAI failing the companies? In other words, a lot of the problems seem to be coming from companies using this form of AI in ways that are ill-advised. There are many things that GenAI can do very well and there are many where the security issues, the privacy issues and the hallucination problems mean it is a horrible fit. I am not hearing the appropriate level of debate and analysis before a decision is made whether to use this form of AI on a project.
The honeymoon phase of generative AI is over A great research report by LucidWorks that reflects what I am also seeing: - Generative AI projects are not going well - Cost and Security is a growing concern - Many companies are not seeing tangible benefits from Generative AI - There is a long path from using a LLM to delivering value in an organization The survey insights are: - Conducted among 1,000 companies across 14 industries. - 63% plan to increase AI spending in 2024, down from 93% in 2023. - 36% will maintain current AI spending levels, up from 6% last year. Challenges noted: - Financial returns on AI projects have been underwhelming. - Only 25% of planned AI investments have been fully implemented. - Significant increase in AI project costs and accuracy concerns. h/t Alastair Roriston Cost and model preferences: - 49% use commercial LLMs like Google's Gemini and OpenAI's ChatGPT. - 30% use a mix of commercial and open-source models. - Shift towards open-source models expected due to performance gains and cost benefits. I won't give away all the great points, but it's a free useful data point for everyone. https://lnkd.in/gBaAtCSm
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Slow Adoption of Generative AI in Business The Wall Street Journal published an article last week titled "Generative AI Isn't Ubiquitous in the Business World—at Least Not Yet" (https://lnkd.in/gTbRiVTk). This article sparked my curiosity about the reasons behind the slow adoption of generative AI (Gen AI) in businesses. After talking to a cross-section of customers over the past year, I've identified several key factors hindering widespread adoption: Cost of AI: While consumer-facing AI tools may seem cheap or even free, they can be expensive for organizations. Take ChatGPT4, for example, which charges $10 per 1 million input tokens and $30 per 1 million output tokens. (Think of tokens as pieces of words; 1,000 tokens are roughly equivalent to 750 words.) For even a simple use case, this can translate to tens of thousands of dollars per month. Lack of Talent: Implementing Gen AI effectively requires a range of skilled and trained professionals. Expertise is needed in Gen AI architecture, implementation, prompt engineering, content moderation, and governance. Privacy and Security Concerns: Companies worry about their organizational information being fed into AI models. They also have concerns about employees inadvertently sending sensitive data outside firewalls through poorly defined prompts. Accuracy and Hallucinations: Customers have encountered (and heard stories of) comical and sometimes alarming instances of AI generating nonsensical or misleading responses. This hesitancy to implement customer-facing applications without robust governance is understandable. Co-piloting with human intervention can further increase costs and decrease ROI (return on investment). Solution: Successful Gen AI implementations require an upfront AI strategy that addresses these concerns. This strategy should ensure sustainable economic value driven by responsible and ethical AI practices. Prevsiant has developed an AI Strategy approach that can help organizations achieve successful and cost-effective Gen AI implementations. You can review it here: https://lnkd.in/gcGN28sg
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The rate of increase of existential terror about AI (with job-loss as proxy) is quite low compared to the rate of increase in corporate concerns arising from the lack of enterprise-grade features. This means that a lot of money is about to be made in AI.
The honeymoon phase of generative AI is over A great research report by LucidWorks that reflects what I am also seeing: - Generative AI projects are not going well - Cost and Security is a growing concern - Many companies are not seeing tangible benefits from Generative AI - There is a long path from using a LLM to delivering value in an organization The survey insights are: - Conducted among 1,000 companies across 14 industries. - 63% plan to increase AI spending in 2024, down from 93% in 2023. - 36% will maintain current AI spending levels, up from 6% last year. Challenges noted: - Financial returns on AI projects have been underwhelming. - Only 25% of planned AI investments have been fully implemented. - Significant increase in AI project costs and accuracy concerns. h/t Alastair Roriston Cost and model preferences: - 49% use commercial LLMs like Google's Gemini and OpenAI's ChatGPT. - 30% use a mix of commercial and open-source models. - Shift towards open-source models expected due to performance gains and cost benefits. I won't give away all the great points, but it's a free useful data point for everyone. https://lnkd.in/gBaAtCSm
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Founder/CEO of companies that help generate profitable revenue for clients. Talks about #ai #artificialintelligence #BAI (before AI) #AAI (after AI) #marketing
The constant introduction of products that have errors and have to be withdrawn from the market is what I hear - consistently - as a key source fueling doubt. C suite teams evaluating AI for their company would never roll out a product only to have it fail so badly that you have to take it down. This category behavior only fosters concern. Name one AI product introduction that didn't have issues in the past 12 months.
The honeymoon phase of generative AI is over A great research report by LucidWorks that reflects what I am also seeing: - Generative AI projects are not going well - Cost and Security is a growing concern - Many companies are not seeing tangible benefits from Generative AI - There is a long path from using a LLM to delivering value in an organization The survey insights are: - Conducted among 1,000 companies across 14 industries. - 63% plan to increase AI spending in 2024, down from 93% in 2023. - 36% will maintain current AI spending levels, up from 6% last year. Challenges noted: - Financial returns on AI projects have been underwhelming. - Only 25% of planned AI investments have been fully implemented. - Significant increase in AI project costs and accuracy concerns. h/t Alastair Roriston Cost and model preferences: - 49% use commercial LLMs like Google's Gemini and OpenAI's ChatGPT. - 30% use a mix of commercial and open-source models. - Shift towards open-source models expected due to performance gains and cost benefits. I won't give away all the great points, but it's a free useful data point for everyone. https://lnkd.in/gBaAtCSm
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AI bubble still Data Engineering Products & Saas Products works well All doesn't need Gen AI
The honeymoon phase of generative AI is over A great research report by LucidWorks that reflects what I am also seeing: - Generative AI projects are not going well - Cost and Security is a growing concern - Many companies are not seeing tangible benefits from Generative AI - There is a long path from using a LLM to delivering value in an organization The survey insights are: - Conducted among 1,000 companies across 14 industries. - 63% plan to increase AI spending in 2024, down from 93% in 2023. - 36% will maintain current AI spending levels, up from 6% last year. Challenges noted: - Financial returns on AI projects have been underwhelming. - Only 25% of planned AI investments have been fully implemented. - Significant increase in AI project costs and accuracy concerns. h/t Alastair Roriston Cost and model preferences: - 49% use commercial LLMs like Google's Gemini and OpenAI's ChatGPT. - 30% use a mix of commercial and open-source models. - Shift towards open-source models expected due to performance gains and cost benefits. I won't give away all the great points, but it's a free useful data point for everyone. https://lnkd.in/gBaAtCSm
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CGO (Chief Glue Officer) | Responsible AI | Health Informatics | Serial Entrepreneur | Podcast host | Orchestrating brilliance by connecting witted minds! Subscribe to my Newsletter & Podcast on design, AI & innovation.
“Generative AI projects are not going well” is a very general and unverifiable statement. What usecase? Which industry? I can say generative AI projects focused on corporate training are going very well. We see a boom in many usecase in the healthcare industry, from clinical annotation to patient engagement. Education is going through a transformative change, for good. Add Perplexity; if you use it, you won't go back to Google your questions as before. Those who thought generative AI will be a silver bullet that solve any problem in any industry would be disappointed. If you pick a valid usecase, and ‘apply’ the technology properly, you get good results.
The honeymoon phase of generative AI is over A great research report by LucidWorks that reflects what I am also seeing: - Generative AI projects are not going well - Cost and Security is a growing concern - Many companies are not seeing tangible benefits from Generative AI - There is a long path from using a LLM to delivering value in an organization The survey insights are: - Conducted among 1,000 companies across 14 industries. - 63% plan to increase AI spending in 2024, down from 93% in 2023. - 36% will maintain current AI spending levels, up from 6% last year. Challenges noted: - Financial returns on AI projects have been underwhelming. - Only 25% of planned AI investments have been fully implemented. - Significant increase in AI project costs and accuracy concerns. h/t Alastair Roriston Cost and model preferences: - 49% use commercial LLMs like Google's Gemini and OpenAI's ChatGPT. - 30% use a mix of commercial and open-source models. - Shift towards open-source models expected due to performance gains and cost benefits. I won't give away all the great points, but it's a free useful data point for everyone. https://lnkd.in/gBaAtCSm
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Business decision engines need 100% quality, accurance and security. This cannot be provided by generative (sub-symbolic) artificial intelligence alone. Semantic (symbolic) artificial intelligence is the missing piece to enable real business solutions. The statistical pattern detection methods used by language models are wonderful conversational interfaces to communicate with users, to say things in a fluent and friendly way, but they don't know what to say. LLMs are alternative tools to traditional enterprise application reports, and like reports, they need quality data. But, in this case, they also require a semantic mesh capable of interacting in natural language with the language model. It is costly to build such a semantic mesh without implementing methods, processes and technology capable of creating and maintaining the semantic mesh with the business language. Companies that have made an early commitment to enrich their data with semantic mesh (active metadata) have a differential competitive advantage at this time. https://lnkd.in/gcP6RGzd
The honeymoon phase of generative AI is over A great research report by LucidWorks that reflects what I am also seeing: - Generative AI projects are not going well - Cost and Security is a growing concern - Many companies are not seeing tangible benefits from Generative AI - There is a long path from using a LLM to delivering value in an organization The survey insights are: - Conducted among 1,000 companies across 14 industries. - 63% plan to increase AI spending in 2024, down from 93% in 2023. - 36% will maintain current AI spending levels, up from 6% last year. Challenges noted: - Financial returns on AI projects have been underwhelming. - Only 25% of planned AI investments have been fully implemented. - Significant increase in AI project costs and accuracy concerns. h/t Alastair Roriston Cost and model preferences: - 49% use commercial LLMs like Google's Gemini and OpenAI's ChatGPT. - 30% use a mix of commercial and open-source models. - Shift towards open-source models expected due to performance gains and cost benefits. I won't give away all the great points, but it's a free useful data point for everyone. https://lnkd.in/gBaAtCSm
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