BEGIN ARTICLE PREVIEW: From a fruit picking drone to a ‘jumping’ weeding robot: what is the most promising field robot concept in 2020? Future Farming and FIRA have organised a competition in which the best or most promising robot is selected. The jury selected 10 candidates from all submissions. The professional jury will assess these 10 entries. But the public can also vote. What do you think is the most promising field robot concept? Take part in the poll and cast your vote! Recently, Future Farming launched the world’s first Field Robot Catalogue. In this robot catalogue you will find 35 field and harvest robots, which you will be able to buy, lease or hire in 2021. All the robots in the catalogue are commercially available. However, there are also many concepts being developed. From tool carriers to picking drones; manufacturers are doing their best to develop practical robots that will help farmers cope with current and future challenges. 10 Field Ro

ServiceNow Inc. is beefing up its artificial intelligence development capabilities with the acquisition today of a company called Element AI Inc. that's widely known as one of the pioneers in the field. Montreal-based Element AI launched back in 2016 as a professional services firm focused on helping traditional enterprises implement machine learning. The startup garnered significant industry attention from the outset thanks in part to its high-profile co-founder, the well-known deep learning researcher Yoshua Bengio, who won the Turing Award in 2018 for his contributions to the field. Element AI has gradually expanded its focus since its launch by creating a fund to support fellow machine learning companies and introducing ready-made AI tools. The company's offerings include Knowledge Scout, a search engine for manufacturers that speeds up the diagnosis and repair of production line issues by giving technicians relevant information about previous incidents with similar characteristics.

K Allado-McDowell had been working with artificial intelligence for years--they established the Artists and Machine Intelligence program at Google AI--when the pandemic prompted a new, more personal kind of engagement. During this period of isolation, they started a conversation with GPT-3, the latest iteration of the Generative Pre-trained Transformer language model released by OpenAI earlier this year. GPT-3 is, in short, a statistical language model drawing on a training corpus of 499 billion tokens (mostly Common Crawl data scraped from the internet, along with digitized books and Wikipedia) that takes a user-contributed text prompt and uses machine learning to predict what will come next. The results of Allado-McDowell's explorations--a multigenre collection of essays, poetry, memoir, and science fiction--were recently published in the U.K. as Pharmako-AI, the first book "co-authored" with GPT-3. By its very nature, the book forces us to ask who is responsible for which aspects of its authorship and to question how we imagine or conceptualize that nonhuman half.

On May 8, 2018, Google I/O was held at Shoreline Amphitheatre in Mountain View, California. If you are wondering what Google I/O is, don't worry, I've got your back. "Google I/O brings together developers from around the globe annually for talks, hands-on learning with Google experts, and the first look at Google's latest developer products." In the Keynote, Sundar Pichai, the CEO of Alphabet Inc. (Google's parent company), shared the then-latest developments that Google had been working on. One of the projects that he spoke about was something that maybe no one saw coming; an application of Artificial Intelligence (AI), soon to be on our own smartphones, that left the world in awe.

Gunrock 2.0 is built on top of Gunrock with an emphasis on user adaptation. Gunrock 2.0 combines various neural natural language understanding modules, including named entity detection, linking, and dialog act prediction, to improve user understanding. Its dialog management is a hierarchical model that handles various topics, such as movies, music, and sports. The system-level dialog manager can handle question detection, acknowledgment, error handling, and additional functions, making downstream modules much easier to design and implement. The dialog manager also adapts its topic selection to accommodate different users' profile information, such as inferred gender and personality. The generation model is a mix of templates and neural generation models. Gunrock 2.0 is able to achieve an average rating of 3.73 at its latest build from May 29th to June 4th.

J. William Middendorf, who lives in Little Compton, served as Secretary of the Navy during the Ford administration. His recent book is "The Great Nightfall: How We Win the New Cold War." Thirteen days passed in October 1962 while President John F. Kennedy and his advisers perched at the edge of the nuclear abyss, pondering their response to the discovery of Russian missiles in Cuba. Today, a president may not have 13 minutes. Indeed, a president may not be involved at all. "Artificial intelligence is the future, not only for Russia, but for all humankind. It comes with colossal opportunities but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world."

The hard part is diversifying the content. So if we just have the same character in an environment doing everything, it's not going to work, right? So how do you actually create hundreds or thousands of variations of that character model with different behavior and things like that? That's been really the core focus of how we're thinking about our technology. You're listening to Gradient Dissent, a show where we learn about making machine learning models work in the real world. Daeil Kim is the co-founder and CEO of AI.Reverie. A startup that specializes in creating high quality synthetic training data for computer vision algorithms. Before that he was a senior data scientist at the New York Times. And before that he got his PhD in computer science from Brown university, focusing on machine learning and Bayesian statistics. He's going to talk about tools that will advance machine learning progress, and he's going to talk about synthetic data. I'm super excited for this. I was looking at your LinkedIn and you have a little bit of an unusual path, right? You did a liberal arts undergrad. Can you say a little bit about... I feel like I come across people quite a lot that want to make career transitions into machine learning and related fields. What was that for you? What prompted you to do it?

Healthcare cybersecurity is in triage mode. As systems are stretched to the limits by COVID-19 and technology becomes an essential part of everyday patient interactions, hospital and healthcare IT departments have been left to figure out how to make it all work together, safely and securely. Most notably, the connectivity of everything from thermometers to defibrillators is exponentially increasing the attack surface, presenting vulnerabilities IT professionals might not even know are on their networks. Get the whole story and DOWNLOAD the eBook now – on us!] The result has been a newfound attention from ransomware and other malicious actors circling and waiting for the right time to strike. Rather than feeling overwhelmed in the current cybersecurity environment, it's important for healthcare and hospital IT teams to look at security their networks as a constant work in progress, rather than a single project with a start and end point, according to experts Jeff Horne from Ordr and G. Anthony Reina who participated in Threatpost's November webinar on Heathcare Cybersecurity. "This is a proactive space," Reina said. "This is something where you can't just be reactive. You actually have to be going out there, searching for those sorts of things, and so even on the technologies that we have, you know, we're, we're proactive about saying that security is an evolving, you know, kind of technology, It's not something where we're going to be finished." Healthcare IT pros, and security professionals more generally, also need to get a firm handle on what lives their networks and its potential level of exposure. The fine-tuned expertise of healthcare connected machines, along with the enormous cost to upgrade hardware in many instances, leave holes on a network that simply cannot be patched. "Because, from an IT perspective, you cannot manage what you can't see, and from a security perspective, you can't control and protect what you don't know," Horne said. Threatpost's experts explained how healthcare organizations can get out of triage mode and ahead of the next attack. The webinar covers everything from bread and butter patching to a brand-new secure data model which applies federated learning to functions as critical as diagnosing a brain tumor. Alternatively, a lightly edited transcript of the event follows below. Thank you so much for joining. We have an excellent conversation planned on a critically important topic, Healthcare cybersecurity. My name is Becky Bracken, I'll be your host for today's discussion. Before we get started, I want to remind you there's a widget on the upper right-hand corner of your screen where you can submit questions to our panelists at any time. We encourage you to do that. You'll have to answer questions and we want to make sure we're covering topics most interesting to you, OK, sure. Let's just introduce our panelists today. First we have Jeff Horne. Jeff is currently the CSO at Ordr and his priors include SpaceX.

Do you think the traffic is bad where you live? Try moving to Boston, where commuters suffer the worst highway congestion in the nation. Residents of the New England city spent an average of 164 hours sitting in their vehicles going nowhere slowly last year, losing as much as $2,291 in personal value for the privilege. And that's nothing compared to the city found to be cursed with the worst highway tie-ups on the planet. Moscow commuters are known to have lost an average of 210 hours each last year to traffic jams.

On Thursday, 26 November, Prof. Andrew Murray, will deliver the Sixth T.M.C. Asser Lecture – 'Almost Human: Law and Human Agency in the Time of Artificial Intelligence'. Asser Institute researcher Dr. Dimitri Van Den Meerssche had the opportunity to speak with professor Murray about his perspective on the challenges posed by Artificial Intelligence to our human agency and autonomy – the backbone of the modern rule of law. A conversation on algorithmic opacity, the peril of dehumanization, the illusionary ideal of the'human in the loop' and the urgent need to go beyond'ethics' in the international regulation of AI. One central observation in your Lecture is how Artificial Intelligence threatens human agency. Could you elaborate on your understanding of human agency and how it is being threatened? In my Lecture I refer to the definition of agency by legal philosopher Joseph Raz. He argues that to be fully in control of one's own agency and decisions you need to have capacity, the availability of options and the freedom to exercise that choice without interference. My claim is that there are four ways in which the adoption and use of algorithms affect our autonomy, and particularly Raz's third requirement: that we are to be free from coercion. First, there is an internal and positive impact. This happens when an algorithm gives us choices, which have been limited by pre-determined values – values that we cannot observe. The second impact is internal and negative. In this scenario, choices are removed because of pre-selected values.