Data Skeptic

It took a massive financial investment for the first large language models (LLMs) to be created.  Did their corporate backers lock these tools away for all but the richest?  No.  They provided comodity priced API options for using them.  Anyone can talk to Chat GPT or Bing.  What if you want to go a step beyond that and do something programatic?  Kyle explores your options in this episode.

We celebrate episode 1000000000 with some Q&A from host Kyle Polich.  We boil this episode down to four key questions: 1) How do you find guests 2) What is Data Skeptic all about? 3) What is Kyle all about? 4) What are Kyle's thoughts on AGI?   Thanks to our sponsorsdataannotation.tech/programmers https://www.webai.com/dataskeptic  

In this episode, we are joined by Amir Netz, a Technical Fellow at Microsoft and the CTO of Microsoft Fabric. He discusses how companies can use Microsoft's latest tools for business intelligence. Amir started by discussing how business intelligence has progressed in relevance over the years. Amir gave a brief introduction into what Power BI and Fabric are. He also discussed how Fabric distinguishes from other BI tools by building an end-to-end tool for the data journey. Amir spoke about the process of building and deploying machine learning models with Microsoft Fabric. He shared the difference between Software as a Service (SaaS) and Platform as a Service (PaaS). Amir discussed the benefits of Fabric's auto-integration and auto-optimization abilities. He also discussed the capabilities of Copilot in Fabric. He also discussed exciting future developments planned for Fabric. Amir shared techniques for limiting Copilot hallucination.

Our guest today is Eric Boyd, the Corporate Vice President of AI at Microsoft. Eric joins us to share how organizations can leverage AI for faster development. Eric shared the benefits of using natural language to build products. He discussed the future of version control and the level of AI background required to get started with Azure AI. He mentioned some foundational models in Azure AI and their capabilities. Follow Eric on LinkedIn to learn more about his work. Visit today's sponsor at https://webai.com/dataskeptic

We are excited to be joined by Aaron Reich and Priyanka Shah. Aaron is the CTO at Avanade, while Priyanka leads their AI/IoT offering for the SEA Region. Priyanka is also the MVP for Microsoft AI. They join us to discuss how LLMs are deployed in organizations.

In this episode, we are joined by Jenny Liang, a PhD student at Carnegie Mellon University, where she studies the usability of code generation tools. She discusses her recent survey on the usability of AI programming assistants. Jenny discussed the method she used to gather people to complete her survey. She also shared some questions in her survey alongside vital takeaways. She shared the major reasons for developers not wanting to us code-generation tools. She stressed that the code-generation tools might access the software developers' in-house code, which is intellectual property. Learn more about Jenny Liang via https://jennyliang.me/  

We are joined by Aman Madaan and Shuyan Zhou. They are both PhD students at the Language Technology Institute at Carnegie Mellon University. They join us to discuss their latest published paper, PAL: Program-aided Language Models. Aman and Shuyan started by sharing how the application of LLMs has evolved. They talked about the performance of LLMs on arithmetic tasks in contrast to coding tasks. Aman introduced their PAL model and how it helps LLMs improve at arithmetic tasks. He shared examples of the tasks PAL was tested on. Shuyan discussed how PAL’s performance was evaluated using Big Bench hard tasks. They discussed the kind of mistakes LLMs tend to make and how the PAL’s model circumvents these limitations. They also discussed how these developments in LLMS can improve kids learning. Rounding up, Aman discussed the CoCoGen project, a project that enables NLP tasks to be converted to graphs. Shuyan and Aman shared their next research steps. Follow Shuyan on Twitter @shuyanzhxyc. Follow Aman on @aman_madaa

In this episode, we have Alessio Buscemi, a software engineer at Lifeware SA. Alessio was a post-doctoral researcher at the University of Luxembourg. He joins us to discuss his paper, A Comparative Study of Code Generation using ChatGPT 3.5 across 10 Programming Languages.  Alessio shared his thoughts on whether ChatGPT is a threat to software engineers. He discussed how LLMs can help software engineers become more efficient.

On the show today, we are joined by Jianan Zhao, a Computer Science student at Mila and the University of Montreal. His research focus is on graph databases and natural language processing. He joins us to discuss how to use graphs with LLMs efficiently.  

Today, we are joined by Rajiv Movva, a PhD student in Computer Science at Cornell Tech University. His research interest lies in the intersection of responsible AI and computational social science. He joins to discuss the findings of this work that analyzed LLM publication patterns. He shared the dataset he used for the survey. He also discussed the conditions for determining the papers to analyze. Rajiv shared some of the trends he observed from his analysis. For one, he observed there has been an increase in LLMs research. He also shared the proportions of papers published by universities, organizations, and industry leaders in LLMs such as OpenAI and Google. He mentioned the majority of the papers are centered on the social impact of LLMs. He also discussed other exciting application of LLMs such as in education.

We are excited to be joined by Josh Albrecht, the CTO of Imbue. Imbue is a research company whose mission is to create AI agents that are more robust, safer, and easier to use. He joins us to share findings of his work; Despite "super-human" performance, current LLMs are unsuited for decisions about ethics and safety.  

On today’s show, we are joined by Thilo Hagendorff, a Research Group Leader of Ethics of Generative AI at the University of Stuttgart. He joins us to discuss his research, Deception Abilities Emerged in Large Language Models. Thilo discussed how machine psychology is useful in machine learning tasks. He shared examples of cognitive tasks that LLMs have improved at solving. He shared his thoughts on whether there’s a ceiling to the tasks ML can solve.

Nieves Montes, a Ph.D. student at the Artificial Intelligence Research Institute in Barcelona, Spain, joins us. Her PhD research revolves around value-based reasoning in relation to norms. She shares her latest study, Combining theory of mind and abductive reasoning in agent‑oriented programming.

We are joined by Maximilian Mozes, a PhD student at the University College, London. His PhD research focuses on Natural Language Processing (NLP), particularly the intersection of adversarial machine learning and NLP. He joins us to discuss his latest research, Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilities.

Our guest today is Vid Kocijan, a Machine Learning Engineer at Kumo AI. Vid has a Ph.D. in Computer Science at the University of Oxford. His research focused on common sense reasoning, pre-training in LLMs, pretraining in knowledge-based completion, and how these pre-trainings impact societal bias. He joins us to discuss how he built a BERT model that solved the Winograd Schema Challenge.

Today, We are joined by Petter Törnberg, an Assistant Professor in Computational Social Science at the University of Amsterdam and a Senior Researcher at the University of Neuchatel. His research is centered on the intersection of computational methods and their applications in social sciences. He joins us to discuss findings from his research papers, ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning, and How to use LLMs for Text Analysis.

In this episode, we are joined by Carlos Hernández Oliván, a Ph.D. student at the University of Zaragoza. Carlos’s interest focuses on building new models for symbolic music generation. Carlos shared his thoughts on whether these models are genuinely creative. He revealed situations where AI-generated music can pass the Turing test. He also shared some essential considerations when constructing models for music composition.

Hongyi Wang, a Senior Researcher at the Machine Learning Department at Carnegie Mellon University, joins us. His research is in the intersection of systems and machine learning. He discussed his research paper, Cuttlefish: Low-Rank Model Training without All the Tuning, on today’s show. Hogyi started by sharing his thoughts on whether developers need to learn how to fine-tune models. He then spoke about the need to optimize the training of ML models, especially as these models grow bigger. He discussed how data centers have the hardware to train these large models but not the community. He then spoke about the Low-Rank Adaptation (LoRa) technique and where it is used. Hongyi discussed the Cuttlefish model and how it edges LoRa. He shared the use cases of Cattlefish and who should use it. Rounding up, he gave his advice on how people can get into the machine learning field. He also shared his future research ideas.

On today’s episode, we have Daniel Rock, an Assistant Professor of Operations Information and Decisions at the Wharton School of the University of Pennsylvania. Daniel’s research focuses on the economics of AI and ML, specifically how digital technologies are changing the economy. Daniel discussed how AI has disrupted the job market in the past years. He also explained that it had created more winners than losers. Daniel spoke about the empirical study he and his coauthors did to quantify the threat LLMs pose to professionals. He shared how they used the O-NET dataset and the BLS occupational employment survey to measure the impact of LLMs on different professions. Using the radiology profession as an example, he listed tasks that LLMs could assume. Daniel broadly highlighted professions that are most and least exposed to LLMs proliferation. He also spoke about the risks of LLMs and his thoughts on implementing policies for regulating LLMs.

We are excited to be joined by J.D. Zamfirescu-Pereira, a Ph.D. student at UC Berkeley. He focuses on the intersection of human-computer interaction (HCI) and artificial intelligence (AI). He joins us to share his work in his paper, Why Johnny can’t prompt: how non-AI experts try (and fail) to design LLM prompts.  The discussion also explores lessons learned and achievements related to BotDesigner, a tool for creating chat bots.