Lise Barnéoud's Hidden Guests shows how this fascinating new field brings with it profound implications for medicine, and even what it means to be human, finds Helen Thomson "We are composed not only of human cells and microbes but also fragments of others " My children were conceived using donated eggs, so you would be forgiven for assuming we share no genetic material. Yet science has proved this isn't entirely true. We now know that during pregnancy, fetal cells cross the placenta into the mother, embedding themselves in every organ yet studied. Likewise, maternal cells, and even those that crossed from my mum to me, can make their way into my kids. And things might get even more chimeric - I have older sisters, so their cells, having passed into my mum during their own gestation, might have then found their way into me and, in turn, into my kids.

A decade ago, Google talked about "thinking in 10x." Whether it's Moore's Law or the current rate of inflation, Nvidia's CEO Jensen Huang has one-upped his fellow Silicon Valley technologists by thinking in million X. According to Huang, it will have a monumental impact on biology and chemistry in the near future. Huang used a good deal of his one-hour-and-42 minute keynote at Nvidia's GPU Technology Conference (GTC) this morning to tout the company's latest GPU architecture, dubbed the H100 Hopper. Built on a thinner 4 nanometer process and featuring 80 billion transistors (68% more than the previous generation A100 GPU), the Hopper immediately becomes the premiere processor to run AI workloads.

Common sense has always been of interest in AI, but has rarely taken center stage. Despite its mention in one of John McCarthy's earliest papers and years of work by dedicated researchers, arguably no AI system with a serious amount of general common sense has ever emerged. Why is that? What's missing? Examples of AI systems' failures of common sense abound, and they point to AI's frequent focus on expertise as the cause. Those attempting to break the brittleness barrier, even in the context of modern deep learning, have tended to invest their energy in large numbers of small bits of commonsense knowledge. But all the commonsense knowledge fragments in the world don't add up to a system that actually demonstrates common sense in a human-like way. We advocate examining common sense from a broader perspective than in the past. Common sense is more complex than it has been taken to be and is worthy of its own scientific exploration.

If there's a silver lining to the COVID-19 pandemic, it's how the world came together to find a solution as one. Throughout the pandemic, technology has enabled global collaboration, as more people shifted to working from home, while science allowed multiple COVID-19 vaccines to be developed and rolled out in record time, despite working remotely. The convergence of governments and industries, particularly biopharmaceuticals, to innovate and create a solution to the crisis was inspiring, but it raises questions around what's needed to see this pace of innovation again. Do we need a global crisis to innovate? Or can biopharma companies forge their own pathways to innovate, while making solutions more affordable and accessible to those who are most affected: patients?

In this special guest feature, Marc Alacqua, CEO and founding partner of Signafire, discusses a useful approach to data – known as data fusion – which is essentially alchemy-squared, turning not just one but multiple raw materials in to something greater than the sum of their parts. It goes beyond older methods of big data analysis, like data integration, in which large data sets are simply thrown together in one environment. Marc is a decorated combat veteran of the U.S. Army Special Operations Forces. For his service during Operation Iraqi Freedom, he was cited for "exceptionally conspicuous gallantry" and awarded two Bronze Star Medals and the Army Commendation Medal for Valor. A 20-year veteran and Lieutenant Colonel, Marc has extensive command experience in both combat and peace time, having commanded airborne and light infantry as well as special operations units.

For over a century, this seemingly reasonable dictum of statistics metastasized into one of science's biggest obstacles, as researchers in all data-driven disciplines became unwilling to say if one thing caused another. But all this has changed with Judea Pearl and his colleagues, whose work over the past three decades has cut through the confusion and established causality-the study of cause and effect-on a firm scientific basis. His work shows how we know simple things, like whether the rain or a sprinkler made a sidewalk wet, as well as how we can answer hard questions, like whether a drug cured an illness. Coauthored with the PhD mathematician turned science writer Dana Mackenzie, THE BOOK OF WHY reveals the far-reaching effects of scientific revolution that is transforming the fields of statistics, epidemiology, cognitive science, and the social sciences, and that will be central to advances in the next generation of artificial intelligence. In the book, Pearl and Mackenzie explore: Why scientists failed for so long to develop a scientific language for expressing cause and effect.

Data is eating the world, one buzzword at a time. In 2017, The Economist declared in "Data is Giving Rise to a New Economy": "Data are to this century what oil was to the last one--a driver of growth and change." And IDC estimated that by 2025 we will create 163 trillion gigabytes of data, ten times more than in 2016. Also in 2017, the Harvard Business Review Press published an updated and expanded 10th anniversary edition of Competing on Analytics: The New Science of Winning by Tom Davenport and Jeanne Harris. More than 150,000 copies of the book have been sold and it has been translated into over 12 languages.

Data is eating the world, one buzzword at a time. In 2017, The Economist declared in "Data is Giving Rise to a New Economy": "Data are to this century what oil was to the last one--a driver of growth and change." And IDC estimated that by 2025 we will create 163 trillion gigabytes of data, ten times more than in 2016. Also in 2017, the Harvard Business Review Press published an updated and expanded 10th anniversary edition of Competing on Analytics: The New Science of Winning by Tom Davenport and Jeanne Harris. More than 150,000 copies of the book have been sold and it has been translated into over 12 languages.

Morphic resonance is a process whereby self-organising systems inherit a memory from previous similar systems. In its most general formulation, morphic resonance means that the so-called laws of nature are more like habits. The hypothesis of morphic resonance also leads to a radically new interpretation of memory storage in the brain and of biological inheritance. Memory need not be stored in material traces inside brains, which are more like TV receivers than video recorders, tuning into influences from the past. And biological inheritance need not all be coded in the genes, or in epigenetic modifications of the genes; much of it depends on morphic resonance from previous members of the species.

Most organizations today aspire to use predictive analytics, artificial intelligence, and machine learning to influence customer behavior and improve business performance. Unfortunately, the process for becoming a "predictive organization" is broken from both a technical and an organizational standpoint. It requires "Unicorns:" Well trained, expensive, rare data scientists and PhDs who are hard to hire and retain. Those teams then invest roughly six months on average cleaning data and building machine learning models, and then have to maintain those models as they degrade over time. Whenever products and consumer behavior change, the models break and need to be re-written.