On a recent afternoon, Mr. Luckey, dressed as if ready for the beach in a Hawaiian-like shirt, shorts and flip-flops, joined other Anduril employees at the company's testing site near Camp Pendleton, a Marine training facility. As the drone took off and swooped between the hills, Mr. Luckey said it could track an object and capture detailed images from seven football fields away. Using many of the artificial intelligence technologies that underpin self-driving cars, Anduril's drones can identify and track vehicles, people and other objects largely on their own. The drones are not armed, but could be useful for guarding bases or reconnaissance. The same sensor technologies that allow the drones to fly on their own could also be used to identify targets on a battlefield.

Artificial intelligence (AI) has become a buzzword in technology in both civilian and military contexts. With interest comes a radical increase in extravagant promises, wild speculation, and over-the-top fantasies, coupled with funding to attempt to make them all possible. In spite of this fervor, AI technology must overcome several hurdles: it is costly, susceptible to data poisoning and bad design, difficult for humans to understand, and tailored for specific problems. No amount of money has eradicated these challenges, yet companies and governments have plunged headlong into developing and adopting AI wherever possible. This has bred a desire to determine who is "ahead" in the AI "race," often by examining who is deploying or planning to deploy an AI system.

The EU's attempts to regulate Artificial Intelligence could be met with future challenges resulting from an agreement on e-Commerce at the level of the World Trade Organisation (WTO), according to a new study published on Tuesday (26 January). Talks have been ongoing since January 2019 between members of the WTO in a bid to agree on global rules to facilitate worldwide e-commerce transactions. However, concerns have been highlighted that the text currently backed by the EU could result in a prohibition on signatories from adopting legislation that obliges firms to provide access to the source code of their software. In this vein, a report published by the Federation of German Consumer Organisations (vzbv) says that a number of EU objectives in the field of digital policy currently on the table could be stifled by the WTO agreement. "The EU's possibility to adopt rules that, for example, mandate external audits of AI systems will be confined to the policy space that is allowed under trade law," the study notes, adding that the European Council and the Commission are responsible for ensuring that trade deals it makes are compatible with internal policy initiatives.

Founder & CEO David Wattel created Multus Medical to deliver innovation through cutting-edge technology. Multus Medical invited Aptus Engineering, Inc. to transform the capability, quality, speed, and cost of their 3D renderings. Aptus delivered a brand new, feature-rich anatomical 3D modeling software that allows technicians to fit models to patient MRIs, AI software that automates the technicians' segmentation process, depict injuries, generate anatomical 3D video renderings, as well as a case management portal, product delivery and access portal, and automated systems to accept and categorize patient documents. In just a few short months, Aptus was able to reduce Multus Medical processing time and costs by over 80% while expanding their capability to offer models and renderings for anatomy other than just the spine. The team at Multus Medical understood that a radically different diagnostic and radiological reporting software with higher sensitivity, specificity, accuracy and positive predictive value for favorable clinical outcomes with proposed interventions was essential for patient-centered, high-quality healthcare service purchasing strategies.

The integration of artificial intelligence systems and machine learning is the next big development in the sphere of information technology. These systems have brought a new wave of advancements in technological developments. It has also transformed the way organizations were using Cyber Security Services to prevent cyber attacks. In old times, cybersecurity was used based on signature pattern matching or rules. With the advent of anti-virus software, companies started to rely on them, but it was used for detecting malware only that matches with the signature or virus definition.

Yoshua Bengio is one of the world's leading experts in artificial intelligence and deep learning. Also known as the father of deep learning, he says that for the world to change for the better with AI, a global shift in how organizations and governments share their research needs to come. In many countries, private companies, government entities, and academic institutions conduct AI research. These places must foster a global culture of open science. These research places the need to rethink how to encourage the development of impactful artificial intelligence.

The prevalence of artificial intelligence (AI) applications in many a tech-driven aspect of our daily lives (everything from driving sustainable agriculture to powering a range of smartphone features) would not have been pronounced if not for the research that went into understanding AI abilities and harnessing them to enhance self-driving cars or mobile apps, to name a handful of use cases. These are powerful, yet functional roles that can be easily comprehended. What's perhaps more exciting is AI's growing potential in sourcing and producing new creations and ideas, from writing news articles to discovering new drugs -- in some cases, far quicker than teams of human scientists would have been able to do. New discoveries and understanding of AI capabilities are ever-evolving, much like the technology itself. Hence many national and private interests are charging ahead with AI research and development in a dizzying assortment of fields, from intelligent automation and robotics to AI-powered databases and systems of national importance.

The Senate parliamentarian ruled Thursday that the provision to increase the minimum wage to $15/hour cannot be included in the broader $1.9 trillion COVID relief package. Why it matters: It's now very likely that any increase in the minimum wage will need bipartisan support, as the provision cannot be passed with the simple Senate majority that Democrats are aiming to use for President Biden's rescue bill.

Millimeter wave vehicular channels exhibit structure that can be exploited for beam alignment with fewer channel measurements compared to exhaustive beam search. With fixed layouts of roadside buildings and regular vehicular moving trajectory, the dominant path directions of channels will likely be among a subset of beam directions instead of distributing randomly over the whole beamspace. In this paper, we propose a deep learning-based technique to design a structured compressed sensing (CS) matrix that is well suited to the underlying channel distribution for mmWave vehicular beam alignment. The proposed approach leverages both sparsity and the particular spatial structure that appears in vehicular channels. We model the compressive channel acquisition by a two-dimensional (2D) convolutional layer followed by dropout. We incorporate the low-resolution phase shifter constraint during neural network training by using projected gradient descent for weight updates. Furthermore, we exploit channel spectral structure to optimize the power allocated for different subcarriers. Simulations indicate that our deep learningbased approach achieves better beam alignment than standard CS techniques which use random phase shift-based design. Numerical experiments also show that one single subcarrier is sufficient to provide necessary information for beam alignment. Millimeter-wave (mmWave) vehicular communication enables massive sensor data sharing and various emerging applications related to safety, traffic efficiency and infotainment [2]-[4]. Yuyang Wang is with Apple Inc., One Apple park way, Cupertino, CA, 95014, USA, email: yuywang@utexas.edu. Nitin Jonathan Myers is with Samsung Semiconductor Inc., 5465 Morehouse Dr, San Diego, CA 92121 USA, email: nitinjmyers@utexas.edu. Nuria González-Prelcic, and Robert W. Heath Jr. are with the Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Dr, Raleigh, NC 27606 USA, email: {ngprelcic, rwheathjr}@ncsu.edu. Part of this work has been presented at IEEE ICASSP 2020 [1]. This material is based upon work supported in part by the National Science Foundation under Grant No. ECCS-1711702, and by a Qualcomm Faculty Award.

This paper presents NEUROSPF, a tool for the symbolic analysis of neural networks. Given a trained neural network model, the tool extracts the architecture and model parameters and translates them into a Java representation that is amenable for analysis using the Symbolic PathFinder symbolic execution tool. Notably, NEUROSPF encodes specialized peer classes for parsing the model's parameters, thereby enabling efficient analysis. With NEUROSPF the user has the flexibility to specify either the inputs or the network internal parameters as symbolic, promoting the application of program analysis and testing approaches from software engineering to the field of machine learning. For instance, NEUROSPF can be used for coverage-based testing and test generation, finding adversarial examples and also constraint-based repair of neural networks, thus improving the reliability of neural networks and of the applications that use them. Video URL: https://youtu.be/seal8fG78LI