A year after China overtook the USA as the number one country for AI investments, the Americans have reclaimed pole position. According to figures published by ABI Research, the United States received 52.3 percent of global AI investments in 2018. In 2018, investments in US-based AI technologies reached a total of $9.7 billion. This represents a staggering 120 percent year-on-year growth. Huge AI investments from companies such as Zoox, Cruise Automation, Zymergen, and Dataminr helped to propel the US back into the number one spot.

New South Wales in Australia has rolled out high-definition cameras to catch people using their cellphones while driving, according to media reports. The technology is intended to target illegal use of cellphones through fixed and mobile trailer-mounted cameras, New South Wales Minister for Roads Andrew Constance was quoted as saying by the CNN. The cameras use artificial intelligence to scan images and zero in on the offenders. The identified images will be verified by authorised personnel, and the images will be securely stored and managed, authorities said. As many as 45 portable cameras will be set up across the Australian state at unknown locations and without warning signs in the next three years, CNN affiliate Sky News Australia reported.

Where the training of machine learning models is concerned, there's always a risk of overfitting -- or corresponding to closely -- to a particular set of data. In point of fact, it's not infeasible that popular machine learning benchmarks like the Arcade Learning Environment encourage overfitting, in that they have a low emphasis on generalization. That's why OpenAI -- the San Francisco-based research firm cofounded by CTO Greg Brockman, chief scientist Ilya Sutskever, and others -- today released the Procgen Benchmark, a set of 16 procedurally-generated environments (CoinRun, StarPilot, CaveFlyer, Dodgeball, FruitBot, Chaser, Miner, Jumper, Leaper, Maze, BigFish, Heist, Climber, Plunder, Ninja, and BossFight) that measure how quickly a model learns generalizable skills. It builds atop the startup's CoinRun toolset, which used procedural generation to construct sets of training and test levels. "We want the best of both worlds: a benchmark comprised of many diverse environments, each of which fundamentally requires generalization," wrote OpenAI in a blog post.

In the last few years, numerous developments have led to a growing awareness of the maturity of Artificial Intelligence. Self-driving cars and personal assistants like Alexa and Siri are some of the consumer-facing technologies that have helped to fuel this awareness. This knowledge can also bring with it a certain dystopian fear about robots and technology "taking over". While we should always strive to be cautious with new technologies, our concerns should also be tempered by understanding the long curve of development that typically precedes these seemingly overnight maturings of technology. I've been reading Artificial Intelligence in Education, a 2019 publication by Wayne Holmes, Maya Bialik, and Charles Fadel, that explores implications of AI in the realm of teaching and learning.

Infantry Soldiers with 1st Battalion, 8th Infantry Regiment, 3rd Armored Brigade Combat Team, 4th Infantry Division, fire an FGM-148 Javelin during a combined arms live fire exercise in Jordan on August 27, 2019, in support of Eager Lion - file photo. Envision a scenario wherein dismounted infantry soldiers are taking heavy enemy fire while clearing buildings amid intense urban combat -- when an overhead drone detects small groups of enemy fighters hidden nearby, between walls, preparing to ambush. As the armed soldiers clear rooms and transition from house to house in a firefight, how quickly would they need to know that groups of enemies awaited them around the next corner? Getting this information to soldiers in seconds can not only decide victory or defeat in a given battle but save lives. What if AI-enabled computer programs were able to instantly discern specifics regarding the threat such as location, weapons and affiliation by performing real-time analytics on drone feeds and other fast-moving sources of information, instantly sending crucial data to soldiers in combat?

Today, we're extremely happy to launch Amazon SageMaker Studio, the first fully integrated development environment (IDE) for machine learning (ML). We have come a long way since we launched Amazon SageMaker in 2017, and it is shown in the growing number of customers using the service. However, the ML development workflow is still very iterative, and is challenging for developers to manage due to the relative immaturity of ML tooling. Many of the tools which developers take for granted when building traditional software (debuggers, project management, collaboration, monitoring, and so forth) have yet been invented for ML. For example, when trying a new algorithm or tweaking hyper parameters, developers and data scientists typically run hundreds and thousands of experiments on Amazon SageMaker, and they need to manage all this manually.

A chatbot is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods. Chatbots are often designed to convincingly simulate how a human would behave as a conversational partner and are used for various practical enterprise use cases including customer service, IT helpdesk, HR or information acquisition (Business Intelligence). ABC Corp uses BotCore's AI chatbot which enables organizations to build and deploy customized AI chatbots. So, let us see how Nathan's life at ABC Corp. has been impacted by chatbots. Nathan has joined as a Marketing Manager.

Original article featured in ReadWrite. Sprint's recent launch of its 5G network in Kansas City, Missouri; Dallas; Houston; and Atlanta offers consumers and entrepreneurs a glimpse into the future. As rapid download speeds and seamless connectivity take hold in cities around the world, tech entrepreneurs will have more opportunities than ever before to make an impact. With 11.5 million people having access to Sprint's network already, imagine what will be possible as that number grows. The healthcare, transportation, agriculture, and manufacturing industries will all be significantly more capable of innovation and growth as these networks take shape.

Facial recognition technology is generating lots of excitement. Yet, it is also very controversial around issues like privacy, reliability, possible bias and lack of regulation. As a result, businesses must beware of the potential disadvantages of facial recognition. There is much criticism about the use of facial recognition technology. Thus, interest groups tend to be very opinionated about it.

Artificial intelligence systems that gather visual cues from the environment, and learn from them, can recognize human faces more accurately than we can. But how do such systems make the leap from pixels to people? Weizmann Institute neuroscientists have now revealed part of the secret: the most advanced AI vision systems evolve as they learn, spontaneously creating connections that bear a surprising resemblance to how neural networks function in the human brain. The research, published in Nature Communications, was performed by Prof. Rafi Malach of the Department of Neurobiology, together with Shany Grossman, a graduate student in the Malach lab. Today's most advanced systems for artificial vision are based on an AI approach called deep convolutional neural networks (DCNNs).