Charting the emergence of AGI?

 I concur with Liv's introduction, the pace of acceleration is staggering. If we, primarily as consumers and often unwitting contributors to AI development there is an insurmountable amount of information to sort, process and come to conclusions on - yet still contribute to the debate. The unfortunate feeling is that none of our contributions will be listened too, never mind being valued.  Schmachtenberger equates FIAT money to 'units of power' where compound interest makes money by itself. "There is now a maximum incentive to turn as much of the world as possible into capital in my holding, and because other people are and could use that against me there is now an arms race to do it faster than that guy.' An apt description of one of the primary drivers of AI. 

Working hypothesis, after a week of peering down the AGI rabbit hole. I started this blog to chart and comment on the rapid changes that are taking place in the development of narrow AI to see what sort of path we are on, and if there is any possibility that we’d see an emergent AGI, over a course of 6 months. Any findings are part of an ongoing hypothesis. Speculations The opportunity for an AGI lies in the distributed links that agent AIs make to perform specific tasks.  No AGI is possible without memory being committed to AI agent results.   Hardware developments, and the mass deployment of, for example Nvidia DGX H100’s ,will be required for agencies to see what the scale of narrow AIs working in cooperation can bring to more general problems. AI self learning, with the assumption of AI improving upon itself, is unproven. There are many assumptions being made in the AI space. Conflating AI intelligence with animal/human intelligence with sentience remains a stretch at best, hyperbo

 It's often the case that more sense is spoken in satire than in countless papers and column inches. A case in point is John Oliver summing up where we are at with the AI hype. It is a well paced piece, getting into the heart of the many issues faced after around 15 minutes. The TayTweets example was both funny and alarming, showing that AI can easily fall for populism/fascism. This is going to be an issue with alignment, as whose values will alignment actually embrace? Human values are still a contested topic, and will remain so, giving national interests. The three laws of robotics will not suffice.  'The right to human review of decisions made fully by computers should not be removed ‘while AI is still in its infancy’, the professional body for IT has warned. Regulation needs to go far beyond this.  I've noted that GPT5 is now going to be an incremental roll out, so by the end of this year, or early next year GPT 4.2, rather than 5 may be rolled out, giving rise to the

 OK there is a long way to go, which in AI terms might equate to months, but the #gen2 generation of Runway ML has promise for pitching movie ideas, advertisements for SMEs and such like, if nothing else. It's hard to comment on this much more until I have the chance to play with it. Lots of questions are raised from the software, and you could certainly imagine the all consuming behemoth Adobe looking to leverage this into Premiere Elements in the future.   

 When GPT can exercise tasks and goals, like in AutoGPT and AgentGPT it becomes of greater utility. One of the interesting things is to look at the ability of it to create reasoned tasks to obtain it's goal. In the video above the agent is asked to 'search for the cheapest Toyota Corolla in the United States'. The agent's own task list is impressive, going beyond it's given remit in useful ways.  I imagine that in a few months time, Open AI will incorporate something similar into it's own interface, given that prompting is currently more difficult than it could be in providing satisfactory goal achievements. If not in Open AI then Microsoft would benefit from building similar directly into their Edge browser or Bing. The use case for such goal orientated AI is broad and wide, but I can see it transforming many research orientated tasks in dramatic ways. 

From the Center for Humane Technology Tristan Harris and Aza Raskin discuss how existing A.I. capabilities already pose catastrophic risks to a functional society, how A.I. companies are caught in a race to deploy as quickly as possible without adequate safety measures, and what it would mean to upgrade our institutions to a post-A.I. world. This presentation is from a private gathering in San Francisco on March 9th with leading technologists and decision-makers with the ability to influence the future of large-language model A.I.s. This presentation was given before the launch of GPT-4. One of the more astute critics of the tech industry, Tristan Harris, who has recently given stark evidence to Congress. It is worth watching both of these videos, as the Congress address gives a context of PR industry and it's regular abuses. "If we understand the mechanisms and motives of the group mind, it is now possible to control and regiment the masses according to our will without their

  Sorting out The Wheat From The Chaff It doesn't take long to find disinformation on the web. That is true in politics as it is in A.I research. There is a plethora of either misunderstood or hyperbolic claims made for both the current state of A.I. applications as well as both a magnitude of dystopic or tech-utopia claims available. There are though certain people, whose career, experience and broad depth of knowledge, together with their position in the tech industry, require time and attention to be given to their talks, papers and popular announcements. Once such person is Goertzel. In this video, a presentation by Goertzel to the Future Mind Institute , where he has an audience of his peers, Goertzel is at his best. There is a difference when someone learned speaks before their peers, rather than appears on a podcast, as the questions tend to be more pointed and the speaker will not over simplify the message for their audience. To be fair, this whole blog site could be de

  AI will change web design. Web design is a creative and technical process that involves planning, creating and maintaining the appearance and functionality of websites. Web designers use various tools and skills to create attractive and user-friendly web pages that meet the needs and expectations of their clients and visitors. However, web design is also a dynamic and evolving field that faces new challenges and opportunities with the advancement of technology. One of the most promising and disruptive technologies that will impact web design in the near future is artificial intelligence (AI). AI is the ability of machines or software to perform tasks that normally require human intelligence, such as learning, reasoning, problem-solving, decision-making and creativity. AI can be applied to various domains and industries, including web design. But how exactly will AI change web design? Here are some possible ways: - AI will automate web design tasks. AI can help web designers save time

AI will change SEO in many ways, both for the better and for the worse. On the positive side, AI can help marketers optimise their content for search engines, by analysing user intent, keywords, relevance, and quality. AI can also help create personalised and engaging user experiences, by delivering the right content to the right audience at the right time. AI can also help automate tedious and repetitive tasks, such as keyword research, link building, and content creation. On the negative side, AI can also pose some challenges and threats to SEO. For one thing, AI can make it harder to rank well on search engines, by creating more competition and raising the standards of quality and relevance. AI can also make it easier for black hat SEO techniques to manipulate search results, by using sophisticated methods such as content spinning, cloaking, and scraping. AI can also make it more difficult to measure and evaluate SEO performance, by introducing more variables and uncertainties. In c

If you are looking for open source chat gpt alternatives, you might be interested in this blog post. In this post, I will introduce you to eight open source projects that aim to provide chatbot functionality using natural language generation models similar to ChatGPT. ChatGPT is a powerful and popular chatbot that can do all sorts of things, but it is not the only example of its kind. Here are some alternatives you might want to try instead. 1. LLaMA The LLaMA project encompasses a set of foundational language models that vary in size from 7 billion to 65 billion parameters. These models are trained on a large and diverse corpus of text, and can generate coherent and fluent text on various topics and domains. LLaMA also provides a web interface where you can interact with the models and test their capabilities. 2. Alpaca Stanford Alpaca claims that it can compete with ChatGPT and anyone can reproduce it in less than 600$. Alpaca is based on a smaller model called GPT-2, which is fine-t

Artificial intelligence (AI) is a powerful technology that can enhance human capabilities, automate tasks, and solve complex problems. However, AI is not a neutral or objective tool. It reflects the values, assumptions, and biases of its creators and users. In this blog post, we will explore some of the inherent biases in AI, how they affect different groups of people, and what can be done to mitigate them. Bias is a deviation from fairness or accuracy in judgement, decision-making, or behaviour. Bias can be intentional or unintentional, conscious or unconscious, explicit or implicit. Bias can also be embedded in data, algorithms, systems, or processes. Bias can have negative impacts on individuals and society, such as discrimination, exclusion, injustice, or harm. Some of the common sources of bias in AI are: Data bias : This occurs when the data used to train or test an AI system is not representative of the real-world population or scenario that the system is intended to serve. For

  Artificial intelligence (AI) is one of the most powerful and transformative technologies of our time. It has the potential to improve many aspects of our lives, from health care and education to entertainment and commerce. However, it also raises some serious ethical and social questions, such as: Will AI lead to homogeneity? Will AI erase the diversity and uniqueness of human cultures, values, and identities? In this blog post, we will explore some of the arguments for and against this possibility, and discuss some of the ways we can ensure that AI is used in a way that respects and celebrates human diversity. What is homogeneity? Homogeneity is the state of being similar or identical in some respect. It can refer to physical characteristics, such as colour or shape, or to abstract qualities, such as beliefs or preferences. Homogeneity can have both positive and negative effects, depending on the context and the degree of similarity. For example, homogeneity can be beneficial when i

Artificial intelligence (AI) can be used in a CRM system to enhance customer service, sales performance, and marketing strategies. Here are some examples of how AI can be applied in a CRM: - AI can enable natural language processing and voice input, such as Siri or Alexa, to allow a CRM system to answer customer queries, solve their problems, and even identify new opportunities for the sales team. Some AI-driven CRM systems can even multitask to handle all these functions and more. - AI can help with sales forecasting by analysing historical data, customer behaviour, and market trends. This can help the sales team make more accurate predictions for future sales figures and determine a success metric. - AI can assist with lead management by automating the process of qualifying and nurturing prospects. It can use chatbots and email bots to understand leads' needs and inform the sales team to improve their performance. With insights gained from these bots, companies can optimise their

  AI can be used in video editing to perform various tasks that would otherwise require a lot of manual work and expertise. Some of the ways in which AI can transform the video editing process are: - Automated Video Editing: AI-powered video editing tools use algorithms that automatically identify and extract the most relevant parts of raw footage, such as objects, people, and backgrounds. Once the relevant parts are identified, the AI algorithms can also automatically assemble the footage into a coherent and engaging video. This can save a lot of time and effort for video editors who need to sift through hours of footage and make creative decisions.  - Facial Recognition: AI can also use facial recognition technology to detect and track the faces of people in videos. This can enable video editors to apply various effects and adjustments to specific faces, such as changing expressions, swapping faces, or adding digital makeup. Facial recognition can also help with lip-syncing, as AI ca

  AI can be incorporated into a DAM (digital asset management) system to enhance its functionality and efficiency. A DAM system is a software platform that stores, organises, and distributes digital assets such as images, videos, audio files, documents, and more. AI can help a DAM system in various ways, such as: Automating the metadata generation and tagging of digital assets, using techniques such as computer vision, natural language processing, and machine learning. This can save time and effort for the users and improve the accuracy and consistency of the metadata. Enabling smart search and retrieval of digital assets, using natural language queries, semantic analysis, and relevance ranking. This can help users find the most suitable assets for their needs and preferences, and avoid duplication or redundancy of assets. Providing content analysis and insights, using data mining, sentiment analysis, and content optimization. This can help users understand the performance and impact o

  Social impact reporting is the process of measuring and communicating the social and environmental effects of an organisation's activities. It is a way of demonstrating the value and impact of an organisation's work to its stakeholders, such as donors, investors, beneficiaries, employees, and the public. Social impact reporting can be challenging for many reasons. Some of the common challenges are: - Defining and measuring the outcomes and impacts of an organisation's activities, which may be complex, long-term, and intangible. - Collecting and analysing data from diverse sources and formats, such as surveys, interviews, case studies, and administrative records. - Communicating the results in a clear, concise, and compelling way that engages the audience and showcases the organisation's achievements. AI can help in generating social impact reporting by providing solutions to these challenges. Some of the ways that AI can help are: - AI can help in defining and measuri

Utilising the excellent Stephen Wolfram's blog post Artificial intelligence (AI) is one of the most powerful and disruptive technologies of our time. It has the potential to transform every aspect of human life, from health care and education to entertainment and commerce. But it also raises some profound ethical and social questions: Will AI replace human workers and make them obsolete? Will AI create new opportunities and challenges for human creativity and collaboration? Will AI pose an existential threat to humanity and its values? These are not easy questions to answer, and there is no consensus among experts and researchers on the future of AI and its impact on society. Some argue that AI will augment human capabilities and enhance human well-being, while others warn that AI will surpass human intelligence and control human destiny. Some envision a utopian scenario where AI will solve all our problems and create a post-scarcity society, while others foresee a dystopian scen