Best suited for deployment of trained AI models in Android and iOS operating systems, TensorFlow Lite provides customers with on-device machine learning capability through mobile-optimized pre-trained models. It’s efficient while having low latency and compatibility for multiple languages which makes it very versatile. Developers can leverage its lightweight and mobile-optimized models to provide on-device AI functionality with minimal latency when implementing TensorFlow Lite in mobile apps.

in 2018 you know it was around four percent of papers were based on Foundation models in 2020 90 were and 00:27:13 that number has continued to shoot up into 2023 and at the same time in the non-human domain it's essentially been zero and actually it went up in 2022 because we've 00:27:25 published the first one and the goal here is hey if we can make these kinds of large-scale models for the rest of nature then we should expect a kind of broad scale 00:27:38 acceleration

It was only by building an additional AI-powered safety mechanism that OpenAI would be able to rein in that harm, producing a chatbot suitable for everyday use.

"There is a robust debate going on in the computing industry about how to create it, and whether it can even be created at all."

Houston, we have a Capability Overhang problem: Because language models have a large capability surface, these cases of emergent capabilities are an indicator that we have a ‘capabilities overhang’ – today’s models are far more capable than we think, and our techniques available for exploring the models are very juvenile. We only know about these cases of emergence because people built benchmark datasets and tested models on them. What about all the capabilities we don’t know about because we haven’t thought to test for them? There are rich questions here about the science of evaluating the capabilities (and safety issues) of contemporary models. 

many other systems that are already here or not far off will have to make all sorts of real ethical trade-offs

Help is coming in the form of specialized AI processors that can execute computations more efficiently and optimization techniques, such as model compression and cross-compilation, that reduce the number of computations needed. But it’s not clear what the shape of the efficiency curve will look like. In many problem domains, exponentially more processing and data are needed to get incrementally more accuracy. This means – as we’ve noted before – that model complexity is growing at an incredible rate, and it’s unlikely processors will be able to keep up. Moore’s Law is not enough. (For example, the compute resources required to train state-of-the-art AI models has grown over 300,000x since 2012, while the transistor count of NVIDIA GPUs has grown only ~4x!) Distributed computing is a compelling solution to this problem, but it primarily addresses speed – not cost.