Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Enterprises continued to accelerate the adoption of AI and machine learning to solve product and business challenges and improve revenues in 2021. Meanwhile, AI startups have experienced significant growth, roping in major investments to improve their product offerings and meet the growing demand for AI solutions across sectors. In fact, data from CB Insights Research shows that while the number of equity funding deals in the global AI space this year is just slightly less than the last (2,384 deals in 2021 versus 2,450 in 2020), the amount of capital invested has almost doubled to $68 billion.

Malware detection is a subset of cybersecurity recently addressed using various machine learning models. The data subject to the model consists of a wide variety of features concerning the network. The models used to ensure the security of the network infrastructure can be linear or complex, depending upon the complexity of the network. If the model fails, malicious software like Ransomware, Spyware, and Trojan can plague a system and, conclusively, the entire network. Attacks like WannaCry have wreaked havoc, caused financial damage, and gained international attention in 2017.

SINGAPORE, September 27, 2022 – Asia/Pacific* spending on AI systems (including hardware, software, and services) will rise from $20.6 billion in 2022 to around $46.6 billion in 2026, according to IDC's latest Worldwide Artificial Intelligence Spending Guide. Artificial intelligence (AI) solutions have become an essential part of process improvement and business decision-making, assisting organizations in keeping up with market viability. IDC forecasts a compound annual growth rate (CAGR) of 23.7 percent for 2021-2026. "Pre-trained natural language and computer vision models have contributed largely to the 1st wave of adoptions. It's time for more organizations to tap into their own data asset and start managing the "data to intelligence" lifecycle. This will become one of the differentiating capabilities for companies to compete in the digital-first era," says Jessie Danqing Cai, Associate Research Director, Artificial Intelligence, IDC Asia/Pacific.

Artificial intelligence (AI) has changed the way organizations identify, respond to and recover from cyberattacks. Concurrently, bad actors are weaponizing AI as both an attack vector and attack surface, adding to the growing list of digital vulnerabilities and blind spots in the insider risk space. In 2019, a reported 14,000 deepfake videos were found online, a 100% increase over those detected just one year prior. One of the most prominent forms of AI exploited by bad actors today is a deepfake. To put it simply, a deepfake is a type of AI-generated media that depicts a person saying or doing something they did not say or do.

Google has created a browser'Easter Egg' of a spacecraft crashing into the web browser when a user searches'NASA Dart', to celebrate the success of the planetary defence test. The graphic shows a probe shooting across the DART-related search results, before it collides and disappears in a cloud of dust, leaving the page askew. The demonstration is triggered by the search terms'NASA DART', 'DART', 'DART probe' or'double asteroid redirection test', the full name of the mission. NASA tweeted about the Easter Egg earlier today, telling followers: 'Your Google search could reveal something smashing! Search for "NASA DART" on to see a demonstration of browser, uh, planetary defense.'

We all started to realize that the rapid development of AI was really going to change the world we live in. AI is no longer just a branch of computer science, it has escaped from research labs with the development of "AI systems", "software that, for human-defined purposes, generates content, predictions, recommendations or decisions influencing the environments with which they interact" (european union definition). The issues of governance of these AI systems – with all the nuances of ethics, control, regulation and regulation – have become crucial, as their development today is in the hands of a few digital empires like them Gafa-Natu-Batx… who have become the masters of real societal choices on automation and on the "rationalization" of the world. The complex fabric intersecting AI, ethics and law is then built in power relations – and connivance – between states and tech giants. But the commitment of citizens becomes necessary, to assert other imperatives than a solutionism technology where "everything that can be connected will be connected and streamlined".

A deep learning model is presented to nowcast the occurrence of lightning at a five-minute time resolution 60 minutes into the future. The model is based on a recurrent-convolutional architecture that allows it to recognize and predict the spatiotemporal development of convection, including the motion, growth and decay of thunderstorm cells. The predictions are performed on a stationary grid, without the use of storm object detection and tracking. The input data, collected from an area in and surrounding Switzerland, comprise ground-based radar data, visible/infrared satellite data and derived cloud products, lightning detection, numerical weather prediction and digital elevation model data. We analyze different alternative loss functions, class weighting strategies and model features, providing guidelines for future studies to select loss functions optimally and to properly calibrate the probabilistic predictions of their model. Based on these analyses, we use focal loss in this study, but conclude that it only provides a small benefit over cross entropy, which is a viable option if recalibration of the model is not practical. The model achieves a pixel-wise critical success index (CSI) of 0.45 to predict lightning occurrence within 8 km over the 60-min nowcast period, ranging from a CSI of 0.75 at a 5-min lead time to a CSI of 0.32 at a 60-min lead time.

Global teams frequently consist of language-based subgroups who put together complementary information to achieve common goals. Previous research outlines a two-step work communication flow in these teams. There are team meetings using a required common language (i.e., English); in preparation for those meetings, people have subgroup conversations in their native languages. Work communication at team meetings is often less effective than in subgroup conversations. In the current study, we investigate the idea of leveraging machine translation (MT) to facilitate global team meetings. We hypothesize that exchanging subgroup conversation logs before a team meeting offers contextual information that benefits teamwork at the meeting. MT can translate these logs, which enables comprehension at a low cost. To test our hypothesis, we conducted a between-subjects experiment where twenty quartets of participants performed a personnel selection task. Each quartet included two English native speakers (NS) and two non-native speakers (NNS) whose native language was Mandarin. All participants began the task with subgroup conversations in their native languages, then proceeded to team meetings in English. We manipulated the exchange of subgroup conversation logs prior to team meetings: with MT-mediated exchanges versus without. Analysis of participants' subjective experience, task performance, and depth of discussions as reflected through their conversational moves jointly indicates that team meeting quality improved when there were MT-mediated exchanges of subgroup conversation logs as opposed to no exchanges. We conclude with reflections on when and how MT could be applied to enhance global teamwork across a language barrier.

Rising global food demand and harsh working conditions make fruit harvest an important domain to automate. Peduncle localization is an important step for any automated fruit harvesting system, since fruit separation techniques are highly sensitive to peduncle location. Most work on peduncle localization has focused on computer vision, but peduncles can be difficult to visually access due to the cluttered nature of agricultural environments. Our work proposes an alternative method which relies on mechanical -- rather than visual -- perception to localize the peduncle. To estimate the location of this important plant feature, we fit wrench measurements from a wrist force/torque sensor to a physical model of the fruit-plant system, treating the fruit's attachment point as a parameter to be tuned. This method is performed inline as part of the fruit picking procedure. Using our orchard proxy for evaluation, we demonstrate that the technique is able to localize the peduncle within a median distance of 3.8 cm and median orientation error of 16.8 degrees.

Machine Learning may push research in precision medicine to unprecedented heights. To succeed, machine learning needs a large amount of data, often including personal data. Therefore, machine learning applied to precision medicine is on a cliff edge: if it does not learn to fly, it will deeply fall down. In this paper, we present Active Informed Consent (AIC) as a novel hybrid legal-technological tool to foster the gathering of a large amount of data for machine learning. We carefully analyzed the compliance of this technological tool to the legal intricacies protecting the privacy of European Citizens.