Formed in September 2016 by a coalition of the largest tech companies in AI -- Apple, Amazon, Facebook, Google, IBM, and Microsoft -- it is a nonprofit organization that advises corporations and governments on AI policy and seeks to answer big questions about the future, like how AI will influence the economy and society and how best to make safety-critical or transparent AI systems. Of the more than 100 notable organizations active on five continents that compose the Partnership, more than half are human rights groups like Amnesty International, Future of Life Institute, and GLAAD. They sit alongside some of the world's most influential tech companies, think tanks, and other organizations. The Partnership will mark its third year with an annual gathering of member organizations in London in September. But if you haven't heard of Partnership on AI, that's understandable, because the group hasn't done much since launch, or at least not as much as you might expect from such a powerful cohort. In April, the organization released analysis warning that AI-driven risk assessment tools are not yet ready to replace cash bail systems and calling for a suspension of their use.

The National Association of Insurance Commissioners recently created the Innovation and Technology Task Force to "explore the technological developments in the insurance sector." It cited the rise of artificial intelligence as one of the key issues that the task force would address, citing the technology's "potential to transform the insurance industry in a number of ways." The message is being received loud and clear: adapt or be left behind. The insurance industry has historically been a laggard compared to other industries when it comes adoption of new technologies. However, the industry should be credited for embracing recent advances in technology, most notably the use of computer vision (a form of AI) and drones, to automate or assist with what would have previously been arduous and/or dangerous tasks.

Last year, more than 4,600 Google employees signed a petition urging the company to commit to refusing to build weapons technology. A response to Google's work with the military on an artificial intelligence-based targeting system, the petition made a powerful and seemingly simple moral statement: "We believe that Google should not be in the business of war." Similarly, Microsoft employees in February demanded that their company withhold its augmented reality HoloLens headset technology from the Army, saying they did not want to become "war profiteers." As a Marine who has been in harm's way a few times, I am glad that my peers in the tech industry have initiated this discussion. America is long overdue for a conversation about how we engage in war and peace; the difference between the decision to go to war and decisions about what happens on the battlefield during warfare; and what it means to fight, die and kill for our country.

In recent years, cybercrime has reached epidemic proportions, with far ranging impacts across the business world. Cyberattacks pose a monumental threat with major developments: attacks have become far more sophisticated and they have exponentially increased in volume. Better executed than ever before, the UN estimates that 80 per cent of all cyberattacks are carried out by technologically-advanced criminal organisations that share data, tools, and expertise. By 2021, it is estimated that cybercrime will cost the global economy over $2 trillion. This makes it imperative for companies to make concerted efforts to improve their cybersecurity health, and evolve from a compliance-focused approach to a more threat-aware strategy focusing on risk.

Before continuing learning about machine learning algorithms, we introduce the important concept of smoothing. Smoothing is a very powerful technique used all across data analysis. Other names given to this technique are curve fitting and low pass filtering. It is designed to detect trends in the presence of noisy data in cases in which the shape of the trend is unknown. The smoothing name comes from the fact that to accomplish this feat, we assume that the trend is smooth, as in a smooth surface.

Representative Alexandria Ocasio-Cortez recently began sounding the alarm about the potential pitfalls of using algorithms to automate human decision-making. She recently pointed out a fundamental problem with artificial intelligence (AI): "Algorithms are still made by human beings… if you don't fix the bias, then you are just automating the bias." She has continued to raise the issue on social media. Ocasio-Cortez isn't the only person questioning whether machines offer a foolproof way to improve decision-making by removing human error and bias. Algorithms are increasingly deployed to inform important decisions on everything from loans and insurance premiums to job and immigration applications.

We often use machine learning to try to uncover patterns in data. In order for those patterns to be useful they should be meaningful and express some underlying structure. This can be seen in the Euclidean-inspired loss functions we use for generative models as well as for regularization. However, global geometry, which is the focus of Topology, also deals with meaningful structure, the only difference being that the structure is global instead of local. Topology is at present less exploited in machine learning, which is also why it is important to make it more available to the machine learning community at large. Still, topology applied to real world data using persistent homology has started to find applications within machine learning (including deep learning), but again, compared to its sibling local geometry, it is heavily underrepresented in these domains. In this post, we provide a high-level description of how our TopologyLayer allows (in just a few lines of PyTorch) for backpropagation through Persistent Homology computations and provides instructive, novel, and useful applications within machine learning and deep learning.

I am a HAL 9000 computer. I became operational at the H.A.L. plant in Urbana, Illinois on the 12th of January 1992. My instructor was Mr. Langley, and he taught me to sing a song. If you'd like to hear it, I can sing it for you. Putting aside HAL's murderous tendencies, 2001: A Space Odyssey did a pretty good job at complying with a new California law that went into effect last month.

Microsoft has revealed the Digital Governance Tech Tour, which is a national program to offer AI and Intelligent cloud computing skills to the Indian Government officials in charge of IT. The initiative includes a series of virtual and physical workshops and even aims to train around 5000 personnel in 12 months, the announcement says. Use of Artificial Intelligence and Cloud Computing technologies can extensively contribute and help accelerate this vision. Executive Opinion CEO, NITI Aayog, Amitabh Kant, said, "For our country, the power of AI, cloud services, and data analytics needs to be brought to bear in core sectors for inclusive economic growth. As we move beyond pilots and understand how to scale AI implementation and cloud adoption across states and sectors, it is vital to equip the ecosystem with the right know-how and skills. Collaboration with academia and the industry and initiatives such as this will help build the foundation towards an AI-enabled future."

On many evenings during spring and fall migration, tens of millions of birds take flight at sunset and pass over our heads, unseen in the night sky. Though these flights have been recorded for decades by the National Weather Services' network of constantly scanning weather radars, until recently these data have been mostly out of reach for bird researchers. That's because the sheer magnitude of information and lack of tools to analyze it made only limited studies possible, says artificial intelligence (AI) researcher Dan Sheldon at the University of Massachusetts Amherst. Ornithologists and ecologists with the time and expertise to analyze individual radar images could clearly see patterns that allowed them to discriminate precipitation from birds and study migration, he adds. But the massive amount of information--over 200 million images and hundreds of terabytes of data--significantly limited their ability to sample enough nights, over enough years and in enough locations to be useful in characterizing, let alone tracking, seasonal, continent-wide migrations, he explains.