We users use Artificial Intelligence (AI) almost every day, often without even realising it i.e. a large amount of the apps and online services we all connect with have a degree of Machine Learning (ML) and AI in them in order to provide predictive intelligence, autonomous internal controls and smart data analytics designed to make the end user User Interface (UI) experience a more fluid and intuitive experience.
That’s great. We’re glad the users are happy and getting some AI-goodness. But what about the developers?
But what has AI ever done for the programming toolsets and coding environments that developers use every day? How can we expect developers to develop AI-enriched applications if they don’t have the AI advantage at hand at the command line, inside their Integrated Development Environments (IDEs) and across the Software Development Kits (SDKs) that they use on a daily basis? Read More: Part 1 … Part 2
Daily Archives: October 1, 2020
The state of AI in 2020: Democratization, industrialization, and the way to artificial general intelligence
After releasing what may well have been the most comprehensive report on the State of AI in 2019, Air Street Capital and RAAIS founder Nathan Benaich and AI angel investor and UCL IIPP visiting professor Ian Hogarth are back for more.
In the State of AI Report 2020, Benaich and Hogarth outdid themselves. While the structure and themes of the report remain mostly intact, its size has grown by nearly 30%. This is a lot, especially considering their 2019 AI report was already a 136 slide long journey on all things AI. Read More
GPT3 and AGI: Beyond the Dichotomy
Earlier this week, I spoke at an interesting online event organized by Khaleej times in the UAE (UAE’s longest running daily English newspaper).
This two-part blog is based on the talk. I addressed a hard topic – and one which I hope sparks some discussion.
To summarize my talk:
- The discussion of whether GPT3 is AGI or not is dominated by either hype or dichotomy.
- We need to think past both these polarizing mindsets because hype misleads discussion and dichotomy stifles discussion.
- If we do so, then what are the implications for AGI?
#human
The Future of AI Part 1
This article will focus on the outlook for AI over the period from 2020 to 2025. The next in the series will consider the longer term potential of AI
The tragedy of lost lives and economic recession caused from the Covid-19 crisis is likely to result in an acceleration of digital transformation and adoption of AI technology. A number of articles and leading firms have made forecasts of accelerated transformation… Read More
The Architectural Implications of Autonomous Driving: Constraints and Acceleration
Autonomous driving systems have attracted a significant amount of interest recently, and many industry leaders, such as Google, Uber, Tesla and Mobileye, have invested large amount of capital and engineering power on developing such systems. Building autonomous driving systems is particularly challenging due to stringent performance requirements in terms of both making the safe operational decisions and finishing processing at real-time. Despite the recent advancements in technology, such systems are still largely under experimentation and architecting end-to-end autonomous driving systems remains an open research question. To investigate this question, we first present and formalize the design constraints for building an autonomous driving system in terms of performance, predictability, storage, thermal and power. We then build an end-to-end autonomous driving system using state-of-the-art award-winning algorithms to understand the design trade-offs for building such systems. In our real-system characterization, we identify three computational bottlenecks, which conventional multi-core CPUs are incapable of processing under the identified design constraints. To meet these constraints, we accelerate these algorithms using three accelerator platforms including GPUs, FPGAs and ASICs, which can reduce the tail latency of the system by 169×, 10×, and 93×respectively.With accelerator-based designs, we are able to build an end-to-end autonomous driving system that meets all the design constraints, and explore the trade-offs among performance,power and the higher accuracy enabled by higher resolution cameras. Read More
#robotics
Rick's Cafe AI 