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It’s been a curious year for AI — with important breakthroughs in NLP and drug discovery tempered by AI’s to-date modest contributions in the fight against Covid-19.

Compiled with my friend Ian Hogarth during the pandemic, this year’s State of AI Report (https://www.stateof.ai/) is a remedy to fears of a new AI winter, and we hope a much needed reminder of the extent of innovation and progress carrying on despite the present circumstances.

However, our report also demonstrates just how much of this innovation is enabled by massive computing infrastructure that is increasingly in the hands of big tech companies.

We need to think carefully about what this means for the future of AI innovation, but also acknowledge that we are seeing more and more AI-first startups implementing the core ideas that emerge from this research. …


In this post, we share key takeaways from our Air Street Summit 2019 on AI-first startup investing, product building, selling companies, and investing in VC funds. If you’re a GP, angel, or LP and you’d like to join us next year, drop us a line!

The AI-first startup investing playbook

AI is a hot topic amongst investors across the capital spectrum. By some counts, over 3.6k “AI startups” in 70+ countries have raised $66B since 2013. The pace is picking up too: $7.4B was raised in Q2 2019 alone. In an environment like this one, it’s important for investors to carefully select signal from noise using an experience playbook (e.g. metrics, frameworks, benchmarks). Just like today’s well-established technology themes such as SaaS, we believe that the AI-first theme will follow a similar trajectory where investors experiment and acquire experience over the years. …


This post was originally published on my blog.

Introduction

As machine learning (ML) grows in popularity amongst large technology companies and startups, businesses of all kinds are taking notice and asking themselves how they can follow suit. There are many resources that help codify the discovery of real-world use cases where it makes technical and commercial sense to use an ML-based solution. For example, inspired by the popular startup business model canvas, the ML Canvas does a great job at structuring this discovery process.

However, less has been written about the common challenges faced by specific business personas as a function of their level of expertise in ML. From my vantage point as an investor in AI-first technology and life science companies with Air Street Capital, I will describe three business personas that I have observed as well as their common challenges, technology stacks, talent requirements and organisational structure. The goal here is to help you define your starting position such that you can make informed decisions as the steps in your ML journey. …


At our 5th Research and Applied AI Summit on Friday this week, I announced that Ian Hogarth and I had released our annual State of AI Report 2019. You can download it from SlideShare here.

Just like our first report, we set out to capture a snapshot of the exponential progress in AI with a focus on developments in the past 12 months. You can consider this report as a compilation of the most interesting things we’ve seen that seeks to trigger an informed conversation about the state of AI and its implication for the future.

Please do drop us a line by hitting reply or tweeting us on @nathanbenaich and @soundboy with comments, feedback and suggestions. …


The Research and Applied AI Summit (RAAIS) is a community for entrepreneurs and researchers who accelerate the science and applications of AI technology. In the lead up to our 5th annual event on June 28th 2019 in London, we’re running a series of speaker profiles to shed more light on what you can expect to learn on the day!

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A driving force behind the blossoming of machine intelligence is next-generation hardware that accelerates training and inference at scale. In 2017, we hosted Graphcore Co-Founder and CTO Simon Knowles. In his talk, Simon motivated the need for novel hardware and outlined the design principles behind the Company’s Intelligence Processing Unit (IPU). …


The Research and Applied AI Summit (RAAIS) is a community for entrepreneurs and researchers who accelerate the science and applications of AI technology. In the lead up to our 5th annual event on June 28th 2019 in London, we’re running a series of speaker profiles to shed more light on what you can expect to learn on the day!

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This year, we’re organising a deep-dive session on how AI methods are transforming the life sciences. We’re thrilled to welcome Jonathan Bloom from the Broad Institute of MIT and Harvard! Jon is a mathematician, engineer, and Institute Scientist at the Broad Institute. …


Life sciences and healthcare are now in the limelight for technologists, in part because AI technologies are well suited to make a positive impact to key workflows. In this essay, I’ll explore 6 areas of life sciences that offer fruitful applications of AI. I hope this will serve as a resource and point of inspiration for those of you who are interested to work in this field.

🔬 Setting the scene

In 2013, the machine learning (ML) research community demonstrated the uncanny ability for deep neural networks trained with backpropagation on graphics processing units to solve complex computer vision tasks. The same year, I wrapped up my PhD in cancer research that investigated the genetic regulatory circuitry of cancer metastasis. Over the 6 years that followed, I’ve noticed more and more computer scientists (we call them bioinformaticians :) and software engineers move into the life sciences. This influx is both natural and extremely welcome. Why? The life sciences have become increasingly quantitative disciplines thanks to high-throughput omics assays such as sequencing and high-content screening assays such as multi-spectral, time-series microscopy. If we are to achieve a step-change in experimental productivity and discovery in life sciences, I think it’s uncontroversial to posit that we desperately need software-augmented workflows. This is the era of empirical computation (more on that here). …


London.AI is a community of AI practitioners from technology companies, startups and academia.

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We’re almost ready to kick off (photo credit: QuantumBlack)

Yesterday evening, we hosted our 14th edition of London.AI at the QuantumBlack offices in central London. Drawing from 412 applicants, we were able to host approx. 100 engineers, researchers, founders, students and operators from organisations such as:


This is an excerpt from my AI newsletter in which I synthesise a narrative that analyses and links important news, data, research and startup activity from the AI world.

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👉 Sign up here so it lands straight in your inbox.

Investments

Q4 2018 has been an active one! Here’s a highlight of the most intriguing financing rounds:

Zymergen, a Bay Area company with a mission to search beyond the bounds of human intuition to deliver novel products and materials, closed a $400M Series C investment led by SoftBank’s Vision Fund (SoftBank proper led their Series B). This business is often held up as a posterchild for how the software industry can intersect with life sciences to accelerate what is an otherwise slow discovery and development process. The key idea is to use machine learning (and other computational methods with lab automation) with real-world and simulated data to navigate a vast search space of potential (in)organic molecules to make discoveries in a much more directed fashion. …


This is an excerpt from my AI newsletter in which I synthesise a narrative that analyses and links important news, data, research and startup activity from the AI world.

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👉 Sign up here so it lands straight in your inbox.

Research Papers

Optimizing Agent Behavior over Long Time Scales by Transporting Value, DeepMind.

How often have you reflected on the outcome a decision that you made a week or a month ago? Learning from our successes and mistakes requires linking actions and consequences over long spans of time (the “credit assignment problem”). Doing so is key to our ability to learn efficiently. In this paper, the authors explore this feature of human nature that is otherwise absent in AI models today that can only reason over short timescales. The authors introduce a new paradigm for reinforcement learning that is based on the following three principles where agents must: 1) Encode and store perceptual and event memories; 2) Predict future rewards by identifying and accessing memories of those past events; 3) Re-evaluate these past events based on their contribution to future reward. Their system, Temporal Value Transport (TVT), integrates these requirements by using neural network attentional memory mechanisms to credit distant past actions for future rewards. …

About

Nathan Benaich

🤓AI-first investing @airstreet, running @raais @londonai @parisai communities, VP @pointninecap, co-author http://stateof.ai, biologist, foodie, et al.

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