Two recent scientific journal papers show what's possible when CRISPR moves from cutting DNA tool to a full-fledged platform -- expanding its toolkit for medicine across R&D, therapeutics, and diagnostics: "Transposon-encoded CRISPR-Cas systems direct RNA-guided DNA integration" in Nature -- by Sanne Klompe, Phuc Vo, Tyler Halpin-Healy, and Samuel Sternberg (of Columbia University) "RNA-guided DNA insertion with CRISPR-associated transposases" in Science -- by Jonathan Strecker, Alim Ladha, Zachary Gardner, Jonathan Schmid-burgk, Kira Makarova, Eugene Koonin, and Feng Zhang (of the Broad Institute) What do these two papers -- both about techniques for getting rid of the need to cut the genome to edit it -- make possible going forward, given the ongoing shift of biology becoming more like engineering? Where are we in the wave of the genome engineering "developer community" building on top of CRISPR with a constantly growing suite of programmable functionalities? a16z bio general partner Jorge Conde and bio deal team partner Andy Tran chat with Hanne Tidnam about these trends -- and these two papers -- in this short internal hallway-style conversation, part of our new a16z Journal Club series. This podcast is also part of our new a16z bio newsletter, which you can sign up for at a16z.com/subscribe

How have we gotten to where were are with machine learning? Where are we going? a16z Operating Partner Frank Chen and Carnegie Mellon professor Tom Mitchell first stroll down memory lane, visiting the major landmarks: the symbolic approach of the 1970s, the "principled probabalistic methods" of the 1980s, and today's deep learning phase. Then they go on to explore the frontiers of research. Along the way, they cover: How planning systems from the 1970s and early 1980s were stymied by the "banana in the tailpipe" problem How the relatively slow neurons in our visual cortex work together to deliver very speedy and accurate recognition How fMRI scans of the brain reveal common neural patterns across people when they are exposed to common nouns like chair, car, knife, and so on How the computer science community is working with social scientists (psychologists, economists, and philosophers) on building measures for fairness and transparency for machine learning models How we want our self-driving cars to have reasonable answers to the Trolley Problem, but no one sitting for their DMV exam is ever asked how they would respond How there were inflated expectations (and great social fears) for AI in the 1980s, and how the US concerns about Japan compare to our concerns about China today Whether this is the best time ever for AI and ML research and what continues to fascinate and motivate Tom after decades in the field The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation. This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments and certain publicly traded cryptocurrencies/ digital assets for which the issuer has not provided permission for a16z to disclose publicly) is available at https://a16z.com/investments/. Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.