Machines might scare policymakers from Brussels to Washington, but artificial intelligence could yield a significant developmental dividend in the developing world. In African markets, the technology behind Alexa and Siri can be harnessed to diagnose illness or address traffic gridlock. One of the most transformative applications of artificial intelligence (AI) is in financial technology, where global investment has risen 38% over the last year. Machine learning, whereby algorithms make predictions and improve based on large amounts of data, is often relegated to the realm technologists and the elite; but for the two billion unbanked adults worldwide, this technology could light a path out of poverty by helping traditional lenders approve loans using hundreds of non-traditional data points. AI has the capacity to add value at the individual, small business, and the large corporate level alike across Africa.

Society's operating system needs an upgrade. The model we have been using is simply not up to the challenges of the Fourth Industrial Revolution. A new era is unfolding at breakneck speed. It has huge potential to address some of the world's most critical challenges, from food security, to reducing congestion in big cities, to increasing energy efficiency, to accelerating cures to the most intractable diseases. But it also raises a host of social and governance issues that need addressing.

SiFive does a quarterly series of tech talks, not necessarily directly to do with SiFive or even RISC-V. For example, last quarter it was Paul Kocher (and if you don't know that name, you need to go and read my post about that talk Paul Kocher: Differential Power Analysis and Spectre). This quarter it was Krste Asanović on Accelerating AI: Past, Present, and Future. This post will cover the past. The present and future have to wait (good title for a movie?).

Artificial intelligence and machine learning saw a significant spike of attention in the past few years – whether it's through partnerships, acquisitions, or in-house developments. The largest financial institutions in the US have been involved in one way or another in bringing artificial intelligence into operations and customer-facing functions. A recent study of 34 major banks across several geographies (US, EU, Singapore, Africa, Australia, India) by MEDICI Team found that 27 out of these 34 banks have implemented AI in their front-office functions in form of a chatbot, virtual assistant, and digital advisor. Some of the most prominent banks in this space across regions are Bank of America, OCBC, ABN Amro, YES BANK, etc. While front-office applications have certainly seen a higher intensity, scope, and adoption, the AI strategy in the US banking industry, in reality, is far more diverse.

Artificial intelligence can help insurance companies and third-party entities to process insurance claims. It can verify data, check for errors and fraud, or find correlations and trends. We count on insurance to be there for us when we need it, and like anything else, it's a system that can be overused and even abused. Artificial intelligence is taking on an important role in the prevention of inaccurate healthcare claims and in innovative claims management. At H2OWorld 2017, in Mountain View, CA, speaker Adam Sullivan of Change Healthcare explained the process of using machine learning to verify healthcare claims data, denied claims, and erroneous payments for hospitals/providers, as well as to predict procedures.

On a recent work trip, I found myself in a swanky-but-still-hip office of a private tech firm. I was drinking a freshly frothed cappuccino, eyeing a mini-fridge stocked with local beer and standing amidst a group of hoodie-clad software developers typing away diligently at their laptops against a backdrop of Star Wars and xkcd comic wallpaper. I wasn't in Silicon Valley: I was in Johannesburg, South Africa, meeting with a firm that is designing machine learning (ML) tools for a local project backed by the U.S. Agency for International Development. Around the world, tech startups are partnering with NGOs to bring machine learning and artificial intelligence to bear on problems that the international aid sector has wrestled with for decades. ML is uncovering new ways to increase crop yields for rural farmers.

Large amounts of labeled data are typically required to train deep learning models. For many real-world problems, however, acquiring additional data can be expensive or even impossible. We present semi-supervised deep kernel learning (SSDKL), a semi-supervised regression model based on minimizing predictive variance in the posterior regularization framework. SSDKL combines the hierarchical representation learning of neural networks with the probabilistic modeling capabilities of Gaussian processes. By leveraging unlabeled data, we show improvements on a diverse set of real-world regression tasks over supervised deep kernel learning and semi-supervised methods such as VAT and mean teacher adapted for regression.

I recently read an article (paywall) in the WSJ about Paul Allen's Vulcan initiative to curb illegal fishing. It's insightful and sheds light on Big Data techniques to address societal problems. After thinking on the story, it struck me that it could be used as a pedagogical tool to synthesize data science with domain knowledge. To me, this stands as the biggest limitation of what I refer to as'data science thinking'– letting technical skills drive the analysis, only later incorporating domain understanding. This post somewhat reads like a case note from business school and the idea is to get data scientists, product managers and engineers talking earlier on in the process.

Paul Steinberg, CTO of Motorola Solutions, speaks to Sam Fenwick about his company's efforts to use AI and machine learning to bring the right data to the user in the right way Paul Steinberg presides over a huge range of research and development activities, ranging from RF engineering and wireless network architectures to drones and robotics. He also manages Motorola Solutions Venture Capital's portfolio and plays a key role in managing Motorola Solutions' intellectual property. One of the things the company is moving towards is a virtual partner – a combination of AI and natural language processing, which allows someone in the field to verbally request information and give commands without talking to a human. Part of the thinking behind this is that people speak faster than they can type, and the need for field workers to stay aware of their surroundings. "The way you and I consume [mobile data] is a slab of black glass, [but the] fundamental imperative [for a police officer, etc] is eyes-up, hands-free. That slab of black glass [is] exactly the opposite: eyes-down, hands-busy. A big part of how we're navigating this problem is around ethnographics and human factors research – living a day in the life of our users and then [working] with the technologists and designers."

One day in late April or early May, Google removed the phrase "don't be evil" from its code of conduct. After 18 years as the company's motto, those three words and chunks of their accompanying corporate clauses were unceremoniously deleted from the record, save for a solitary, uncontextualized mention in the document's final sentence. Google didn't advertise this change. In fact, the code of conduct states it was last updated on April 5th. The "don't be evil" exorcism clearly took place well after that date. Google has chosen to actively distance itself from the uncontroversial, totally accepted tenet of not being evil, and it's doing so in a shady (and therefore completely fitting) way.