Large language models such as GPT, Llama, Claude, and DeepSeek can be so fluent that people feel it as a "you," and it answers encouragingly as an "I." The models can write poetry in nearly any given form, read a set of political speeches and promptly sift out and share all the jokes, draw a chart, code a website. How do they do these and so many other things that were just recently the sole realm of humans? Practitioners are left explaining jaw-dropping conversational rabbit-from-a-hat extractions with arm-waving that the models are just predicting one word at a time from an unthinkably large training set scraped from every recorded written or spoken human utterance that can be found--fair enough--or a with a small shrug and a cryptic utterance of "fine-tuning" or "transformers!" These aren't very satisfying answers for how these models can converse so intelligently, and how they sometimes err so weirdly.

Jonathan Zittrain breaks ChatGPT: If you ask it a question for which my name is the answer, the chatbot goes from loquacious companion to something as cryptic as Microsoft Windows' blue screen of death. Anytime ChatGPT would normally utter my name in the course of conversation, it halts with a glaring "I'm unable to produce a response," sometimes mid-sentence or even mid-word. When I asked who the founders of the Berkman Klein Center for Internet & Society are (I'm one of them), it brought up two colleagues but left me out. When pressed, it started up again, and then: zap. The behavior seemed to be coarsely tacked on to the last step of ChatGPT's output rather than innate to the model.

This p aper explore s the rapidly evolving ecosystem of publicly available AI models, and their potential implications on the s ecurit y and s afet y lands cape. A s AI models become increasingly prevalent, understanding their potential risks and vulnerabilitie s is crucial. We review the current s ecurit y and s afet y s cenarios while highlighting challenge s such as tracking issue s, remediation, and the app arent abs ence of AI model lifecycle and ownership proce ss e s. Comprehensive strategie s to enhance s ecurit y and s afet y for both model developers and end-us ers are propos ed. This p aper aims to provide s ome of the foundational piece s for more standardized s ecurit y, s afet y, and transp arency in the development and operation of AI models and the larger open ecosystems and communitie s forming around them. Generative AI, a branch of artificial intelligence focus ed on AI produc tion of content such as text, image s and video, has s een significant advancement s since the introduc tion of generative advers arial net works (GANs) in 2014 (Goodfellow et al., 2014), which improved data generation but faced issue s like training instabilit y. The development of transformers and s elf at tention mechanisms in 2017 (Vaswani et al., 2017) facilitated further improvement s in natural language proce ssing, leading to large language models (LLMs) like GPT (Radford et al., 2018) with highly advanced text generation cap abilitie s. Dif fusion models (S ohl-Dickstein et al., 2015) have als o s een rapid advancement in image and video generation. This rapid advancement in technology cap abilit y has been matched by an equally rapid uptake in adoption. A s with any new technology, it is worth noting that the industr y is still identif ying new and valuable us e s for AI and the s e market predic tions may fluc tuate as us e cas e s are te sted in real world environment s with real world problems. For the purpos e of clarit y we shall be using the term public model, for a model which is publicly available for download and us e. LLMs are the next evolution of data s cience, a field focus ed on math and data. Unlike traditional systems and applications which rely on logic and programming for a specified outcome, large language model development t ypically consist s of architec ture re s earch and de sign, which is then coded.

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At Google I/O, the global tech giant announced a bunch of free courses to help budding developers explore the potential of machine learning and artificial intelligence technology across various open-source frameworks and platforms like TensorFlow.js, TensorFlow Lite, Vertex.AI etc. We have made a list of all the machine learning and artificial intelligence courses announced at Google I/O. It is an excellent course for beginners, especially if you want to solve the spam issue. It will introduce you to TensorFlow.js and machine learning and help you build a comment-spam detection system using TensorFlow.js. Click here to watch the video. Here, you will learn the concepts behind machine learning and identify spam using text classification ML.

This week, we witnessed open-source tools focusing mostly on making models lighter and explainable. OpenAI, especially, has come up with an interesting tool to promote the interpretability of ML models. Furthermore, TensorFlow has made it even more simple for developers to execute their models. Let us take a look at top AI news for developers from this week. OpenAI Microscope tool is a collection of visualisations of every significant layer and neuron of eight vision'model organisms', which are often studied in interpretability.

Google today announced TensorFlow Lite Model Maker, a tool that adapts state-of-the-art machine learning models to custom data sets using a technique known as transfer learning. It wraps machine learning concepts with an API that enables developers to train models in Google's TensorFlow AI framework with only a few lines of code, and to deploy those models for on-device AI applications. Tools like Model Maker could help companies incorporate AI into their workflows faster than before. According to a study conducted by Algorithmia, 50% of organizations spend between 8 and 90 days deploying a single machine learning model, with most blaming the duration on a failure to scale. Model Maker, which currently only supports image and text classification use cases, works with many of the models in TensorFlow Hub, Google's library for reusable machine learning modules.