The potential for advances in information-age technologies to undermine nuclear deterrence and influence the potential for nuclear escalation represents a critical question for international politics. One challenge is that uncertainty about the trajectory of technologies such as autonomous systems and artificial intelligence (AI) makes assessments difficult. This paper evaluates the relative impact of autonomous systems and artificial intelligence in three areas: nuclear command and control, nuclear delivery platforms and vehicles, and conventional applications of autonomous systems with consequences for nuclear stability. We argue that countries may be more likely to use risky forms of autonomy when they fear that their second-strike capabilities will be undermined. Additionally, the potential deployment of uninhabited, autonomous nuclear delivery platforms and vehicles could raise the prospect for accidents and miscalculation. Conventional military applications of autonomous systems could simultaneously influence nuclear force postures and first-strike stability in previously unanticipated ways. In particular, the need to fight at machine speed and the cognitive risk introduced by automation bias could increase the risk of unintended escalation. Finally, used properly, there should be many applications of more autonomous systems in nuclear operations that can increase reliability, reduce the risk of accidents, and buy more time for decision-makers in a crisis.

This paper proposes a tentative and original survey of meeting points between Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have been developing quite separately in the last three decades. Some common concerns are identified and discussed such as the types of used representation, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal understanding. Then some methodologies combining reasoning and learning are reviewed (such as inductive logic programming, neuro-symbolic reasoning, formal concept analysis, rule-based representations and ML, uncertainty in ML, or case-based reasoning and analogical reasoning), before discussing examples of synergies between KRR and ML (including topics such as belief functions on regression, EM algorithm versus revision, the semantic description of vector representations, the combination of deep learning with high level inference, knowledge graph completion, declarative frameworks for data mining, or preferences and recommendation). This paper is the first step of a work in progress aiming at a better mutual understanding of research in KRR and ML, and how they could cooperate.

In this paper, we introduce the increasing belief criterion in association rule mining. The criterion uses a recursive application of Bayes' theorem to compute a rule's belief. Extracted rules are required to have their belief increase with their last observation. We extend the taxonomy of association rule mining algorithms with a new branch for Bayesian rule mining~(BRM), which uses increasing belief as the rule selection criterion. In contrast, the well-established frequent association rule mining~(FRM) branch relies on the minimum-support concept to extract rules. We derive properties of the increasing belief criterion, such as the increasing belief boundary, no-prior-worries, and conjunctive premises. Subsequently, we implement a BRM algorithm using the increasing belief criterion, and illustrate its functionality in three experiments: (1)~a proof-of-concept to illustrate BRM properties, (2)~an analysis relating socioeconomic information and chemical exposure data, and (3)~mining behaviour routines in patients undergoing neurological rehabilitation. We illustrate how BRM is capable of extracting rare rules and does not suffer from support dilution. Furthermore, we show that BRM focuses on the individual event generating processes, while FRM focuses on their commonalities. We consider BRM's increasing belief as an alternative criterion to thresholds on rule support, as often applied in FRM, to determine rule usefulness.

Managing Marketing is a weekly podcast hosted by TrinityP3. Each one is a conversation with a marketing thought-leader, professional, practitioner or expert on the issues and topics of interest to marketers and business leaders everywhere. In this special series, TrinityP3's Anton Buchner, discusses the rise of Artificial Intelligence and the impact it is having on marketing. Jay Henderson is the Senior Vice President of Product Management at Acoustic (formerly Watson Customer Engagement – purchased from IBM by Centerbridge Partners, and rebranded in 2019 as Acoustic). He talks about how machine learning algorithms should be seen as working together with marketers. Offering options and solutions for marketers to assess and consider, rather than being seen as a distrustful'black box' of solutions running rampant by themselves. Welcome to Managing Marketing, a weekly podcast where we sit down and talk with marketing thought leaders and experts on the issues and topics of interest to marketers and business leaders everywhere. To discuss this I'm sitting down today with Jay Henderson. Jay is the senior vice-president of product management for Acoustic. Thanks, I'm really excited to be here. You've just flown in so you've got over your jetlag? I got here a couple of days ago. We're here today in Sydney to launch the Acoustic brand and the company into the Australian market.

Technology develops differently in Japan. While US tech giants have been grabbing artificial intelligence headlines, a business AI sector has been quietly maturing in Japan, and it is now making inroads into America. Today we sit down again with Miku Hirano, CEO of Cinnamon, and we talk about how exactly this happened. Interestingly, Cinnamon did not start out as an AI company. In fact, when Miku first came on the show, the company had just launched an innovative video-sharing service. Today, we talk about what lead to the pivot to AI and why even a great idea and a great team is no guarantee of success. We also talk about some of the changing attitudes towards startups and women in Japan, the kinds of business practices AI will never change, and Miku give some practical advice for startups going into foreign markets. It's a great discussion, and I think you will really enjoy it. Welcome to Disrupting Japan, straight talk from Japan's most successful entrepreneurs. Today, we're going to sit down and talk about artificial intelligence with Miku Hirano of Cinnamon. Now, Cinnamon is actually a great example of a successful Japanese startup pivot. When we first sat down with Miku four years ago, she had an innovative micro-video sharing company called Tuya and really, you should go back and listen to that episode. I've put a link on the show notes and it was really a good one.

In its 4 year existence the Israeli start-up Taranis has seen huge growth. Taranis started as a tool to provide farmers with the information to detect and prevent crop disease, weeds and insect damage based on weather forecasts gathered from aerial surveillance. The technology was then further developed by adding visual layers from satellites, planes and drones and leveraged with AI capabilities. Taranis also created a one-of-a-kind, patented hardware that can capture accurate images at a high resolution from a plane flying at 160 km/h, such as a specific insect on a leaf from 200 feet above ground. Taranis CEO Ofir Schlam says the future of the precision farming industry is looking bright, with thousands of start-ups emerging within the last 10 years. According to him, smart farming is projected to create a massive impact on the agricultural economy in the near future and will be dependent on precision technologies, such as the adoption of automated practices and indoor urbanised farming.

After adjusting your search parameters, press Enter on your keyboard or click on the red magnifying glass to run your query again. Murdoch plan to reunite Fox with News Corp. finds few fans Several analysts said the potential recombination is unlikely to solve one of the key problems facing Fox and News Corp. -- low valuations relative to their peers. Norah O'Donnell will become anchor and managing editor of the CBS Evening News and Gayle King is getting two new morning show co-hosts as CBS News seeks to boost the programs' ratings and put a tumultuous, scandal-scarred period behind it. In February, the YourNewsWire page on Facebook was at its peak popularity, boosted by its salacious post claiming that Justin Trudeau, the Canadian prime minister, was Fidel Castro's love child. Brian Ross, the veteran ABC News investigative correspondent who embarrassed the network late last year with an on-air report suggesting former National Security Adviser Michael Flynn had been told by President Donald Trump to make contact with Russian officials during the 2016 campaign for the Oval...

I love tech, innovation, AI etc. I worked with it for decades in Latin America, not in Silicon Valley with a positive ecosystem, investors etc. OK?;-) We Human Beings developed robots to serve us, not vice versa.

Key Questions Answered in this Report: • What is the estimated global artificial intelligence in agriculture market size in terms of value during the period 2018-2024? Global Artificial Intelligence (AI) in Agriculture Market Forecast, 2019-2024 The Global Artificial Intelligence (AI) in Agriculture Market Analysis projects the market to grow at a significant CAGR of 28.38% during the forecast period from 2019 to 2024. The reported growth in the market is expected to be driven by the increasing need to optimize farm operation planning, growing demand to derive insights from emerging complexities of data-driven farming, and rising development of autonomous equipment in agriculture. Artificial intelligence has emerged to be a strong driving force behind the growth of data-driven farming.Regions and countries where agriculture is the major source of livelihood and sustenance, the artificial intelligence technology has led to greater profitability in the farms of those economies. The reduction in expenditure and resultant positive RoI with AI's integration in farm equipment and operations has even reached above 30% in a few countries.

WalkMe, a CA-based digital adoption platform, secured $90 million in the funding round. Vitruvian Partners led the latest funding round while Insight Partners also noted their participation. Founded in 2011, CA-based, WalkMe offers a digital adoption platform. The platform is powered by Machine Learning, Artificial Intelligence, automation, and analytics capabilities. To date, the company has raised a total of $307.9