Microsoft Corp. co-founder Bill Gates spoke out against protectionism in technological research around topics like artificial intelligence, arguing that open systems will inevitably win out over closed ones. In conversation with Bloomberg News editor-in-chief John Micklethwait at the New Economy Forum in Beijing on Thursday, Gates was skeptical about the idea that ongoing U.S.-China trade tensions could ever lead to a bifurcated system of two internets and two mutually exclusive strands of tech research and development. "It just doesn't work that way," said the software pioneer. "AI is very hard to put back in the bottle," Gates said, and "whoever has an open system will get massively ahead" by virtue of being able to integrate more insights from more sources. Citing Microsoft's AI research in Beijing, Gates pondered the rhetorical question of whether it was producing Chinese AI or American AI.

Lots of math, science, technology and business roles involve, say, operating a power plant to maximize energy efficiency, or running an ad campaign to minimize cost per click,

According to Business Insider, more than 50% of the total costs for delivering goods is attributable to what is known as "last mile delivery" – the point at which the package finally arrives at the buyer's door. In a recent study by Global Industry Analysts, the last mile delivery market worldwide is expected to reach over $35 Billion by 2025. Last mile delivery is the most expensive and time-consuming part of the shipping process, either due to lack of density and long distances in rural areas or traffic congestion in urban ones. The idea of using Unmanned Aerial Vehicles (UAVs) – or drones – for last mile delivery is gaining popularity. The use of drones to deliver parcels has the potential to significantly decrease delivery costs – no driver, truck or congestion – and expand coverage areas.

Man-made brainpower (AI) will soon be at the core of each major technological framework on the planet to manage and get to your strategic information. Only a couple of uses are cyber and homeland security, anti-money laundering, payments, financial markets, biotech, healthcare, marketing, natural language processing (NLP), computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT). Artificial Intelligence is turning into a significant staple of innovation, scarcely any individuals comprehend the advantages and weaknesses of AI and Machine Learning innovations. While machine intelligence is sure to assume a key role in the making of cutting edge frameworks in a wide assortment of industry areas sooner rather than later, it is especially applicable in quickly developing businesses, for example, ICT, manufacturing and transportation. Over the globe, mobile operators are preparing to deploy the fifth era of 3GPP mobile wireless networks (5G).

The next arms race will be fought not in megatons but in milliseconds. When the primary players can destroy each other 100 times over, it comes down to who can do so faster. Of course, that is a vast oversimplification of the race occuring right now, particularly between the United States and China for superiority in artificial intelligence programs and related technology. The U.S. is currently leading, but according to the National Security Commission on Artificial Intelligence in its recent report to Congress, China is projected to overtake the U.S. in research and development spending within ten years. Of course, spending primacies can change. If, for example, military and political priorities move less resources to a space force and more to practical AI integration into current fighting forces, the Chinese edge could be blunted or reversed.

Running a bootstrapped startup is tough, so Alexander Rinke, co-CEO and co-founder of Celonis, tried a quirky way to cut marketing costs: send handwritten letters to would-be clients. He and his team also figured it could be more effective, thinking a typical formal letter to a top exec would routinely be opened and thrown in the garbage by an executive assistant. "We thought if we hand-write the letter and the address on the envelope an executive assistant can't just open it because it might be a personal letter, from a grandmother, a father-in-law or somebody," he told Business Insider. They sent 1,500 handwritten letters to executives of German businesses, dozens of which turned into solid sales leads. Nowadays, Celonis, which uses AI to help businesses evaluate and fix IT processes, doesn't have to worry too much about using offbeat cost-cutting tricks.

Welcome to the October edition of our best and favorite articles in AI that were published this month. We are a Paris-based company that does Agile data development. This month, we spotted articles about AI that can solve physics problems, paint portraits, judge criminals, play video games and even recognize smells! Let's start, as usual, with the comic of the month: The DeepMind's bot AlphaStar managed to enter the Grandmaster league in Starcraft II. This league is the highest of the seven ranked leagues of the game.

Seventy years ago, science fiction writer Isaac Asimov imagined a world where robots would serve humans in countless ways, and he equipped them with built-in safeguards now known as Asimov's Three Laws of Robotics, to prevent them, among other goals, from ever harming a person. Guaranteeing safe and fair machine behavior is still an issue today, says machine learning researcher and lead author Philip Thomas at the University of Massachusetts Amherst. "When someone applies a machine learning algorithm, it's hard to control its behavior," he points out. This risks undesirable outcomes from algorithms that direct everything from self-driving vehicles to insulin pumps to criminal sentencing, say he and co-authors. Writing in Science, Thomas and his colleagues Yuriy Brun, Andrew Barto and graduate student Stephen Giguere at UMass Amherst, Bruno Castro da Silva at the Federal University of Rio Grande del Sol, Brazil, and Emma Brunskill at Stanford University this week introduce a new framework for designing machine learning algorithms that make it easier for users of the algorithm to specify safety and fairness constraints.

Fascination with AI shows no sign of abating in Europe. While established companies may still be slow to pick the technology up, new start-ups in the sector continue to grow – in France by as much as 38% this year already. There is also no shortage of investment, with $4bn pumped into the sector globally in Q3 compared to $2.8bn in the same period last year, while the UK continues to set the research and development standard in Europe with 623 AI-related patents. It's often said numbers like this are not intended to discourage entrepreneurs, but to motivate them to work smarter and harder. Unfortunately, it's more than just hard work that sets a company up for success – and for those who are looking to scale up their AI venture, the best lessons to be learned can be drawn from those who've been in the same position – and thrived.

Canada has received more than its usual share of attention for its AI capabilities. The country was either prescient or lucky in continuing to fund neural networks research when the US retreated from it in the 1970s and 80s. As a result, Canadian researchers like Geoffrey Hinton, Yann LeCun (who is French-American, but worked with Hinton's group in Toronto), and Yoshua Bengio pushed forward the methods we now call "deep learning." These three researchers won the 2018 Turing Award--often called the Nobel equivalent for computer science. Canada is also known in AI for its collegial, public/private ecosystems.