OPINION Could the Hallucination of Generative AI Large Language Models LLM be emerging evidence of a dangerous new trend toward the AGI Dunning-Kruger Effect ? blog.biocomm.ai

It is for this reason that in the last two years many large organizations have publicly declared that they’ll adhere to AI principles or ethics guidelines. Harvard University ana- lysed the AI principles (Fjeld and Nagy, 2020) of the first 36 organizations in the world that published such guidelines. Harvard found nine categories of consideration, including human values, professional responsibility, human control, fairness & non-discrimination, transparency & explainability, safety & security, accountability, privacy and human rights. The not-for-profit organization Algorithm Watch maintains an open inventory of AI Guidelines4 with currently over 160 organizations. And the European Com- mission presented its Ethics Guidelines for Trustworthy AI (HLEG, 2019) in April 2019.

It is important to note that AI training and inferencing has been going on long since before generative AI got the market’s attention recently. Clients who develop and deploy AI models often elect to purchase GIGABYTE’s industry-leading G-Series GPU Servers, the E-Series Edge Servers, and R-Series Rack Servers. Once the AI has been properly trained and tested, it’s time to move on to the inference phase. The AI is exposed to massive amounts of new data to see if its trained neuron network can achieve a high accuracy for the intended result. In the case of generative AI, this could mean anything from writing a science fiction novel to painting a picture of an ultra-realistic scenery with various artistic elements.

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He seeks to understand the biological basis of consciousness by bringing together research across neuroscience, mathematics, AI, computer science, psychology, philosophy and psychiatry. He was the 2017 President of the British Science Association (Psychology Section). Michael Spranger is the COO of Sony AI Inc., Sony’s strategic research and development organization established April 2020. Concurrent to Sony AI, Michael also holds a Senior Researcher position at Sony Computer Science Laboratories, Inc., and is actively contributing to Sony’s overall AI ethics strategy.

My company, Telefónica, published its AI Principles5 in 2018, committing to the use of AI systems that are fair, transparent and explainable, human-centric, and with privacy & security. Everything mentioned above is of concern, because AI and data are applied with the intention to improve or optimize our lives. Think of AI-based cyber- attacks, terrorism, influencing important events with fake news, etc. (Brundage, 2018). If robots become more autonomous and learn during their ‘lifetime,’ what sort of relationship should be allowed between robots and people? In Asia, robots are already taking care of elderly people, offering companionship and stimulation.

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To stay ahead of market trends, you need to to inform, align and inspire the business effectively. As an insights expert, you’re sharing consumer reports to your Marketing and Business Teams regularly. But it’s hard to share vast amounts of data in a way that is both memorable and actionable.We help turn insights experts from librarians into storytellers. Using our collaborative knowledge zones, you can create compelling insights that increase the usage of consumer insights in daily decision making by 60%. We recently saw the case involving the UK telecom provider Talk Talk that was sanctioned by the Information Commissioner with a fine of £ 400,000 for not having prevented a cyber-attack which led to the access to data of over 150,000 customers. But what would have happened if Talk Talk was not able to determine whether a cyberattack had occurred and all of sudden its system starts taking “unusual” decisions?

As a consequence, the system could discriminate against women in its recommendations. Author of A Data-Driven Company, Richard Benjamins, talks about the social and ethical challenges of AI and big data. Keep up to date with the latest insights from Market Logic as well as all our company news in our free monthly newsletter. Market Logic dramatically lowers research spending, enables faster information retrieval, and reduces the workload of insights & analytics teams. Market Logic creates an infrastructure in which data, information, and knowledge are shared.

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It has moved quickly to adopt Aveni Detect, the AI and Natural Language Processing (NLP)-based technology platform… Perhaps one day we will develop general AI and the machine will both know what it is saying and – crucially – know that it is a thing that is saying something to us. When that happens we’ll look back on the current fuss over LLMs the way astronomers consider astrology – there was some good data collection and analysis but the fundamental model was so disconnected from reality that it was dangerous.

In safety-critical domains, such as health, justice and transportation, defining ‘accuracy’ is not a technical decision, but a domain or even a political decision. The model uses a combination of natural language processing, deep learning, and statistical algorithms to generate text that is contextually relevant and grammatically correct. Microsoft has moved quickly; after the deal with Open AI, they announced their intention to integrate the technology across their entire ecosystem, and so far, they have been true to their word. As of March 2023, users can access the latest open AI model ChatGPT-4 through Open AI or through Microsoft’s Bing Search engine. While this functionality is still at an early stage the results are extremely significant.

Such misinformation can prove to be dangerous as evidenced by the short lived Galactica, a LLM developed by Meta which shut down after three days as it was capable of generating incorrect, racist and dangerous information. Tweets reported that the Galactica genrative ai was capable of generating Wikipedia entries on the benefits of suicide and on the benefits of being white. The blogging sector is also witnessing a major shift as queries related to high search volumes and now generate generative results.

As per rule four, however, a proportionate approach is required, depending on the nature and importance of the decision. Looking at the broader HE ecosystem, if generative AI models can be trained to answer students’ questions with a higher degree of accuracy than a human tutor might, the cost of education – and therefore cost barriers – could fall very significantly. Improved online learning offers with far more personalisation would accelerate access to education in developing countries.

Online publishers currently rely heavily on the referral traffic generated from search engines; users visiting web pages for more information are monetized through ad impressions. Earlier methods of training relied on ‘labelled’ data and were supervised by human programmers – which is to say, a lot of hand-holding was necessary. But recent advancements have made it possible for the AI to engage in self-supervised or semi-supervised learning using unlabelled data, greatly expediting the process.

• It takes note when it is praised for a job well done, and it is especially attentive when the human criticizes its output.
• In the case of generative AI, this could mean anything from writing a science fiction novel to painting a picture of an ultra-realistic scenery with various artistic elements.
• Different types of tokens (both fungible or non-fungible) can be leveraged here to construct governance frameworks and voting rights.
• Machine learning finds whatever pattern there is in the data, regardless of specific norms and values.
• But it’s hard to share vast amounts of data in a way that is both memorable and actionable.We help turn insights experts from librarians into storytellers.

In summary, generative AI is a broader field that includes NLP and NLG as specific areas of focus. NLP enables computers to process and understand human language, while NLG specifically focuses on generating human-like text. Both NLP and NLG are important components of generative AI, enabling systems to understand and generate text in a wide range of applications.

Generative AI can craft human-like responses and create output such as text, imagery, and music. It can learn, solve problems, create simulations, and produce complex digital models; it’s predicated on the digestion of huge swathes of inputted data. The clarification on the level of details to be disclosed is important because otherwise individuals might understand the logic followed by the machine and act in a manner so that they can take unfair advantages. However, the above also means that it is not possible to adopt a GDPR compliant privacy information notice that would cover any type of machine learning or artificial intelligence technology.