Low Returns on AI Projects

Google has made several announcements relating to their storage technologies. Its Spanner SQL database, used in Google Search, Gmail and YouTube, will now include graph and vector support, notably with the use of the GraphQL standard, along with full-text and vector search features. These additions will help with implementing retrieval augmented generation (RAG) support in models. Google’s BigQuery now includes Gemini support to help with data analytics. In another development, an agreement between Oracle and Google saw Google agree to host Oracle Exadata and Autonomous database services in Google data centers. The approach is win-win, since Oracle users will continue to pay them licensing fees.

In a talk given at the Gartner Data & Analytics Summit in Sydney, a Gartner VP claims that many organizations are struggling to create value from generative AI (GenAI) projects. The reasons cited include poor data quality management within organizations, which hampers training or fine-tuning AI models with organizational data, and also the up-front and recurring annual financial costs of training and operating models. In a survey by Gartner in late 2023 of 822 companies trying to develop new business models based on GenAI, only 15.8% of respondents reported a revenue increase due to GenAI, 15.2% reported cost savings and 22.6% reported improved overall productivity. The VP warned that investment in GenAI requires a high-tolerance for indirect and future returns over an immediate return on investment, and that benefits are very company, use case, role and workforce specific. He also claimed that 30% of GenAI projects are abandoned after the proof-of-concept stage. The five main GenAI use cases observed were coding assistants, personalized sales content creation, virtual assistants, RAG-enhanced document search, and models tailored to financial or medical services.

This post from Github presents an interesting experience report on how GenAI models were used to help the organization move from manual to automated trend identification and implement improved feature identification in user tickets. Hitherto, analysis of user tickets was manual – and therefore error-prone, time-consuming, and not as precise as it should be. The post cites a Business Review study which estimates that data scientists spend 80% of their time on manual data collection, organization and classification. Github used the open-source BERTopic model to analyze user data. This model is particularly suited to dynamically identifying topics in documents, and this was combined with a dedicated clustering algorithm to aggregate similar topics into clusters. In a second stage, GPT-4 was used to summarize topic clusters to get to the essentials of user feedback. Noteworthy points of the Github approach is that user feedback data was not used to train a model, which addresses privacy concerns. The GPT-4 model was nonetheless continually optimized by human feedback and A/B testing (where two model versions are compared for the quality of their outputs), by refining input prompts and adjusting internal model parameters like temperature (which controls the degree of randomness in model outputs) and maximum number of tokens.

This article proposes ethical rules to govern the use of GenAI in law practices. The rules insist on understanding the risks and the benefits of GenAI technology, ensuring that work created with tools are reliable and consistent with an acceptable legal, ethical and professional standard of care, and ensuring that attorney-client privilege and other legally protected information remains confidential and secure. The first risk cited is that of hallucinations, as there have been several publicized cases of content used in court cases discovered to be GenAI created. Another risk cited is that of bias, because the prompt represents the legal position of the user. This can result in the output ignoring opposing legal argument and precedence. GenAI services have been known to leak information, and the risk of compromising client confidentiality is also cited. Further, the author advises legal workers to consult with clients before using GenAI services for their cases. A related issue is that information shared with the GenAI service could actually compromise legal arguments used to defend other clients. Yet another issue raised concerns fees: a lawyer is expected to charge a client a reasonable amount, but if a service like ChatGPT is doing a lot of the work, then fee estimation might need to be revised.

This TechCrunch article is an on-going article that presents the latest developments around OpenAI and ChatGPT. The article has been running for several months. One of the key recent developments is the partnership of OpenAI with Apple which will see ChatGPT integrated into Apple’s Siri. OpenAI also made a first release of GPT-4o’s updated realistic audio responses, with the Sky voice (purportedly that of the US actress Scarlett Johansson) removed. The company is also testing SearchGPT – a search engine designed to give “timely responses” to queries and which proposes pertinent follow-up queries to the user. The company is also announcing CriticGPT, a model trained on GPT-4 that aims to find errors in other models’ outputs and to help humans whose task is to search for incorrect output in model testing. Also in June, OpenAI released its latest small AI model, GPT-4o mini, replacing GPT-3.5 Turbo as the smallest OpenAI model. Finally, the article reports on undisclosed claims that OpenAI needs to spend 7 billion USD to train and operate ChatGPT in 2024, and that the company could lose up to 5 billion USD in operating costs.

This post reports on a survey from Stack Overflow on the use and perception of GenAI in software development. The survey analyzed responses from 65’000 developers in 185 countries. One interesting result is that 70% of professional developers do not see AI as a threat to their jobs. On the contrary, the feeling is that GenAI is increasing the need for developers since mastering AI is a newly required skill. 76% of developers are using AI in 2024, compared to 70% in 2023. The advantage of the technology is seen as reduced time to program boilerplate code, leaving more time for the hard programming problems. On the other hand, only 43% of developers trust the accuracy of AI-generated code, and getting the AI to understand context being the biggest challenge. While many developers have now experimented with AI code-generation tools, many developers are disappointed by their experience.

This MIT Technology Review article looks at the phenomenon that AI is increasingly being used to write the content of Web pages. The issue is model collapse, the idea that the behavior of AI models degrade when they are trained on data that it itself created by AI. This problem might get worse as more sites become populated with AI-generated content and as legitimate sites block search agents from AI firms looking for training data. The article describes an experiment at the University of Oxford where researchers fine-tuned a model from Wikipedia data, and then redid the fine-tuning with the output of the first iteration. This process was repeated nine times. The researchers defined a perplexity score to measure the nonsensicalness of the output, and high perplexity scores are reported for much of the output after the iterations. Another issue observed is that data about minority groups is highly distorted because of the high number of content samples about the groups in the training data. One way to combat model collapse is to develop models that attribute higher weights to human generated content compared to content of unknown origin.

This article looks at the use of AI in the area of job applications. It cites a report from Jobscan that claims that 97% of Fortune 500 companies use automated hiring systems to process CVs. This is partly due to the huge increase in job applications that stems from job openings being announced on-line, and with the applications being submitted on-line. The article cites a 2023 study from iCIMS which found that 57% of college graduates have used AI to help write CVs and cover letters, and 25% of Gen Z applicants have used AI. Some companies try to exclude AI tool usage by techniques like asking applicants to submit an introductory video about themselves. On the other hand, the article argues, the use of AI is helping to "level the playing field", since job applicants have to navigate an increasingly challenging job market.

President Biden invited seven top executives from AI companies to the White House in July 2023. He since issued an executive order on the safe use of AI, and wants Congress to pass regulation on AI safety. Also last year, Tech firms committed, though not in any binding way, to the development of Responsible AI. This article mentions how AI now features in the 2024 election debate in the US. Silicon Valley is known to be unhappy with antitrust regulation and data protection laws because they believe regulation stifles innovation. In relation to AI, Donald Trump signed an executive order when he was president for more research into AI and if he wins the November elections, he wants a "Manhattan Project" to support military AI, to throw out the Biden executive order and to reduce AI regulation. Many of the Silicon Valley executives are publicly supporting Trump in the elections for these reasons.