The artificial intelligence company Anthropic launched a National Security and Public Sector Advisory Council in efforts to deepen ties with Washington and allied governments as AI becomes increasingly central to defence. The San Francisco-based start-up announced the new panel on Wednesday. The council's launch underscores AI firms' growing efforts to shape policies and ensure their technology supports democratic interests amid global competition. Anthropic's new effort comes as rivals, such as OpenAI and Google DeepMind, step up engagement with governments and regulators on AI safety, though neither has announced a dedicated national security advisory council. Anthropic's council brings together former senators and senior officials from the US Department of Defense, intelligence agencies, as well as the Departments of Energy and Justice.

The global battle for AI dominance is underway, according to author Paul Scharre, a former Army Ranger and current VP and director of studies at the Center for New American Security -- a think tank specializing in national security issues. Scharre previously served as a strategic planner at the Office of the Secretary of Defense, working to establish policies on unmanned and autonomous systems and emerging weapons technologies, and established DOD policies on intelligence, surveillance, and reconnaissance programs. In his latest book, "Four Battlegrounds: Power in the Age of Artificial Intelligence," Scharre explores how the international battle for the most powerful AI technology is changing global power dynamics. That battle, he says, is a global competition to seek the best and most efficient data, computing hardware, human talent, and institutions adopting AI technology -- which will determine the next global superpower. In your new book, you argue there's a battle for global power going on in the form of a revolution brought about by artificial intelligence.

Dual-use technology as applied to AI and associated technologies has become a central element in today's great power competition. In military and policy communities across the globe, it is believed that the countries who lead in the development of emerging technologies such as AI, machine learning and quantum computing, will garner economic, military, and political strength for decades. Simultaneously, regulating the export of these items is important to policy making in many countries which are concerned with maintaining international peace and security and preventing the proliferation of weapons of mass destruction. While the US still holds its record as the most important global technological innovator, China has made substantial progress in developing critical technologies. Current advances might be used for beneficial purposes, but many are concerned that AI innovation may also be harnessed with harmful intentions.

'… Europe is a unique aspiration. Since then questions of new technologies and European values have been at the forefront of political discussions in Brussels and member states regarding Artificial Intelligence (AI), including preparations for the forthcoming AI Act and recently adopted Digital Services Act. These discussions have not only addressed technocratic questions of economic indicators and legal instruments but also involved soul-searching and reflections on European identity: What is Europe? What does Europe stand for? And how does Europe want to project its identity and power to the rest of the world?

Pager was feverishly moving a joystick as he played his favorite game, Pong, but it was merely a force of habit--the joystick itself was not actually connected to anything. Yet the ball moved from paddle to paddle. He was using his thoughts to play, through use of his direct neural connections from his newly implanted Neuralink device. Pager isn't your typical 9-year-old, though: He is a macaque monkey and will provide valuable information to the company owned by Elon Musk, so it may eventually move forward with human testing for this invasive medical device. In fact, his gaming performance was at a live Neuralink event, in which people were first introduced to a working implanted model.

Artificial intelligence (AI) and machine learning are deeply transformative technologies, poised to change customer-business relationships and create clear-cut business advantages. However, while we're beginning to understand the possibilities of these technologies, it's also clear we're just scraping the surface -- the broadest-reaching implications, advances, and economic impact of AI and machine learning are only now starting to take shape. A recent Gartner analysis predicts AI-driven global business value will reach $1.2 trillion in 2018, a 70 percent increase year-over-year. By 2022, Gartner reports, this number is expected to hit $3.9 trillion. As organisations strive to become experience businesses, AI technologies will help them improve digital interactions and deliver more personalised customer experiences, transform their operations and compete at the top level.

Efforts to develop artificial intelligence (AI) are increasingly being seen as a global race, even a new Great Game. Apart from the race between countries to become more competent and establish a competitive advantage in AI, enterprises are also in a contest to acquire AI talent, leverage data advantages, and offer unique services. In both cases, success would depend on whether AI solutions can be democratized and distributed across sectors. The global AI race is unlike any other global competition, as the extent to which innovation is being driven by governments, the corporate sector or academia differs substantially from country to country. On average, though, the majority of innovations so far have emerged from academia, with governments contributing through procurement, rather than internal research and development.

Efforts to develop artificial intelligence (AI) are increasingly being framed as a global race, or even a new Great Game. In addition to the race between countries to build national competencies and establish a competitive advantage, firms are also in a contest to acquire AI talent, leverage data advantages, and offer unique services. In both cases, success will depend on whether AI solutions can be democratized and distributed across sectors. The global AI race is unlike any other global competition, because the extent to which innovation is being driven by the state, the corporate sector, or academia differs substantially from country to country. On average, though, the majority of innovations so far have emerged from academia, with governments contributing through procurement, rather than internal research and development.

China's momentum and challenges in artificial intelligence investments yield telling lessons for its worldwide observers. China's ambition in artificial intelligence is often framed as a tech rivalry between two important centers for digital innovation -- the east coast of China and the U.S. West Coast. But this rivalry is an undercard for the main event: AI's largest and most enduring contributions will be in non-technology sectors, as traditional companies unlock value in regions far away from Silicon Valley and the string of coastal cities that constitute China's innovation corridor. For non-tech sectors, our research indicates that Chinese companies' approach to adopting AI differs from those in other regions, raising important questions. Buoyed by the country's latest five-year plan and enabled by centralized data, these companies are investing aggressively in AI and adapting their business models to accommodate for AI's potential.

Increased information sharing through short and long-range skip connections between layers in fully convolutional networks have demonstrated significant improvement in performance for semantic segmentation. In this paper, we propose Competitive Dense Fully Convolutional Networks (CDFNet) by introducing competitive maxout activations in place of naive feature concatenation for inducing competition amongst layers. Within CDFNet, we propose two architectural contributions, namely competitive dense block (CDB) and competitive unpooling block (CUB) to induce competition at local and global scales for short and long-range skip connections respectively. This extension is demonstrated to boost learning of specialized sub-networks targeted at segmenting specific anatomies, which in turn eases the training of complex tasks. We present the proof-of-concept on the challenging task of whole body segmentation in the publicly available VISCERAL benchmark and demonstrate improved performance over multiple learning and registration based state-of-the-art methods.