Freeing robots from cages, and pioneering the next generation of industrial robotics

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Industrial Robotics is currently a $40B global market, growing at double-digit rates per year as a wide variety of industries drive initiatives around digital transformation. Despite a rich ecosystem of OEMs developing significant improvements to hardware over the past few decades, the core cognitive capabilities of robots have mostly remained the same during that time.

The vast majority of robots in the field today are meticulously programmed to follow a static set of instructions to complete some specific task.

These robots are set up with custom code that dictate point-by-point guidance for that task, and then left to repeatedly perform that exact set of instructions until the task changes and the robots need to be reprogrammed again. …

95% of the eligible patients for a clinical trial are never considered. It’s time for a transformation.

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Buzz in the Pharma industry continues regarding the application of machine learning to drug discovery. Industry leaders like Novartis and breakout startups like Recursion Pharmaceuticals are working hard to accelerate drug research with computational techniques.

However, the science of drug discovery is only half of the picture. Over 50% of the spend/time/pain associated with bringing a drug to market actually resides within clinical trial operations. This is the critical stage where promising treatments are delivered to patients to generate enough clinical evidence for commercial approval.

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Phase 1 through Phase 3 clinical trials are the stages in which Pharmaceutical companies select research hospitals (sites), enroll patients, and collect data to build a base of clinical evidence for submission to regulatory bodies. This process is a critical step to validate pre-clinical scientific discoveries/theories, and is a phase that can take far too long. …

A counterintuitive approach to biological design is unlocking life-saving therapies and novel materials

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The Humbling Number & Biological Complexity

The humbling number is 10⁶¹⁴. To put that in perspective, your odds of winning the Powerball is on the order of 10⁸. There are 10⁸⁰ total atoms in the universe. To go from 10⁸⁰ atoms to 10⁸¹…takes 10 universes. To go to 10⁸³, 1000 universes. In comparison to those reference points, 10⁶¹⁴ is an unknowably large number.

There are estimated to be 10⁶¹⁴ possible combinations of the human genome.

Biology is the most complex problem space that humans have ever attempted to understand. We should be humbled by the sheer beauty and scale of that complexity, the way that we are when we look at the Grand Canyon or Half Dome (if the Grand Canyon were the depth of near-infinite universes). …

Advancing protein therapies using computational biology

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Obvious Ventures has been fortunate to work alongside some of the leaders in computational biology for many years. Having been investors in both Zymergen and Recursion Pharmaceuticals since Series A, we have seen firsthand how powerful this next-generation approach to biology can be. These companies are leading the charge on a new method of investigating biology, one that is premised on machine learning driven by experimental data instead of root-cause oriented scientific research.

Informed by the underlying philosophy of these leaders, we have been looking for an alternative approach to protein engineering that leverages computational techniques for over two years.

Introduction to Proteins

Genes are the functional units that dictate biological function, organism makeup, and even behavior. Chemically, genes are segments of DNA sequences that encode instructions to make proteins, which in turn carry out most cellular functions. To make a protein, DNA is first transcribed and processed into messenger RNA (mRNA), and the mRNA is then translated into proteins. …

Tech is breaking out of the Silicon Valley sandbox, transforming traditional, trillion-dollar industries in the process.

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The history of Silicon Valley, and what continues to define the technology sector zeitgeist today, can mostly be reduced to a simple tenet: digital solutions to digital problems.

Think servers, security, e-commerce, mobile apps, marketing software, adtech, and digital media. We are entering the 4th decade of tech startups rolling out the internet, making it simultaneously accessible and addictive while monetizing the usage. This tried-and-true playbook has been perfected as Silicon Valley has transformed from a field of apple orchards into a global economic force.

Despite the magnitude of this business success, these “digitally-native” categories ultimately represent a small slice of the economy, and an even smaller share of what matters in the world. …

Driving towards a safe, high performance future for AI

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DarwinAI CEO Sheldon Fernandez, credit David Bebee, Waterloo Region Record.

A University of Toronto paper published in 2012 called “ImageNET Classification with Deep Convolutional Neural Networks” outlined a fundamentally different approach to the ImageNET computer vision competition and went on to outperform previous approaches by over 30%. Since then, the deep learning approach to clustering and classification using neural networks has been applied towards many real-world applications including Go, autonomous vehicles, NLP, physics engines, and more.

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Neural net-based approaches have been able to deliver super-human performance on ImageNet

However, neural networks come with significant challenges. For one, they are computationally burdensome to train and to run due to their complex and large architectures. They require high performance computing systems (such as supercomputer clusters and GPU arrays, if not emerging custom ASICs such as Intel Nervana) and are difficult to deploy on the edge because of this inefficiency. Additionally, deep neural networks require machine learning experts to delicately design and fine-tune the large, complex architectures without a full awareness of the inner workings. …

Designer in Residence at Obvious Ventures

One of the core tenants that we follow at Obvious Ventures is that technology is by nature an agnostic tool. There are no inherent barriers that constrain digital technology to a specific subset of the economy. And yet, the majority of startup creation and venture capital funding have concentrated on a small number of digitally native industries: online marketing, server software, media, and eCommerce — digital solutions to digital problems. The Obvious Ventures team has always held the belief that there is tremendous opportunity in breaking this trend and extending the scope of what technology can enable to greenfield, non-digital industries. …

Democratizing tax filing and financial advice

At Obvious Ventures, we have set our mandate to find and support companies that leverage technology for good — companies that use the latest, deeply technical, breakthroughs to solve tough, often ignored, problems in our economy. In recent years, this pursuit has repeatedly brought us to the evaluation of applied AI as one such breakthrough.

The re-emergence of artificial intelligence has hit peak prominence in the technological zeitgeist, but has simultaneously emerged as a hot-button socioeconomics issue as well. From politics to business sections of major news outlets, debate surrounding the downstream effects of AI/automation on employment and the US economy rages on. While there are valid points on both the job creation vs.

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Obvious co-founder and managing director James Joaquin addressing the Kairos crowd on the floor of NYSE.

It has been a pleasure working with the Kairos team for the past few years to support their mission: empowering a network of young entrepreneurs working on the great challenges of our society. Sound familiar and world positive? We thought so too.

For the previous Kairos K50 Summit in 2015, we cooked up a way for Obvious Ventures to officially be involved — as a startup judge during the conference, and a sponsor of a $50K investment that we deemed the #worldpositive prize.

This was an investment in a company that best represented our focus: applying technology towards a systemic challenge in the world. …

About

Nan Li

GP @ Obvious; technology, music, culture enthusiast

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