The mainstream approach for filter pruning is usually either to force a hard-coded importance estimation upon a computation-heavy pretrained model to select "important" filters, or to impose a hyperparameter-sensitive sparse constraint on the loss objective to regularize the network training. In this paper, we present a novel filter pruning method, dubbed dynamic-coded filter fusion (DCFF), to derive compact CNNs in a computation-economical and regularization-free manner for efficient image classification. Each filter in our DCFF is firstly given an inter-similarity distribution with a temperature parameter as a filter proxy, on top of which, a fresh Kullback-Leibler divergence based dynamic-coded criterion is proposed to evaluate the filter importance. In contrast to simply keeping high-score filters in other methods, we propose the concept of filter fusion, i.e., the weighted averages using the assigned proxies, as our preserved filters. We obtain a one-hot inter-similarity distribution as the temperature parameter approaches infinity. Thus, the relative importance of each filter can vary along with the training of the compact CNN, leading to dynamically changeable fused filters without both the dependency on the pretrained model and the introduction of sparse constraints. Extensive experiments on classification benchmarks demonstrate the superiority of our DCFF over the compared counterparts. For example, our DCFF derives a compact VGGNet-16 with only 72.77M FLOPs and 1.06M parameters while reaching top-1 accuracy of 93.47% on CIFAR-10. A compact ResNet-50 is obtained with 63.8% FLOPs and 58.6% parameter reductions, retaining 75.60% top-1 accuracy on ILSVRC-2012. Our code, narrower models and training logs are available at https://github.com/lmbxmu/DCFF.

Jui Khankari, a 17-year-old Hinsdale Central High School student, founded AInspire, to teach the applications of artificial intelligence, or AI, to students …

Vertigo is a common but under-treated medical condition that affects up to 40% of people at some point in their lives. Currently, the diagnosis and treatment of vertigo-causing conditions is done primarily by specialists who represent only 1% of the doctors in Australia, but AI could change this. Dr Allison Young has recently received a junior fellowship from The Garnett Passe and Rodney Williams Memorial Foundation to address this. Her project, in collaboration with clinicians, data scientists and statisticians, will use machine learning and AI techniques to develop a "virtual expert" diagnostic tool to assist the diagnosis of vertigo-causing conditions in the hospital emergency room, general practice, and in outpatient clinics.

Advanced cybersecurity capabilities are essential to safeguard software, systems, and data in a new era of cloud, the internet of things, and other smart technologies. In the real estate industry, for example, companies are concerned about the potential for hijacked elevators, as well as compromised building management and heating and cooling systems. According to Greg Belanger, vice president of security technologies at CBRE, the world's largest commercial real estate services and investment company, securing the enterprise has grown more complex--security teams must be familiar with controls and hardware on new devices, as well as what version of firmware is installed and what vulnerabilities are present. For example, if a heating, ventilation, and air-conditioning (HVAC) system is connected to the internet, he questions, "Is the firmware that's running the HVAC system vulnerable to attack? Could you find a way to traverse that network and come in and attack employees of that company?" Understanding enterprise vulnerabilities are crucial to safeguard physical assets but investing in the right tools can also be a challenge, says Belanger. "Artificial intelligence and machine learning need large sets of data to be effective in delivering the insights," he explains. In the era of cloud-first and industrial internet of things, the perimeter is becoming far more fluid. By applying AI and machine learning to data sets, he says, "You start to see patterns of risk and risky behavior start to emerge." Another priority when securing physical assets is to translate insights into metrics that C-suite leaders can understand, to help boost decision-making. CEOs and members of boards of directors, who are becoming more security savvy, can benefit from aggregated scores for attack surface management. "Everybody wants to know, especially after an attack like Colonial Pipeline, could that happen to us? How secure are we?" says Belanger.

A free and open internet is under attack, according to Sundar Pichai, the head of Google. In a wide-ranging interview with the BBC, the Google CEO said an open internet –information online being equally free and available to everybody – has been a'tremendous force for good' that is'taken for granted'. While Mr Pichai did not directly refer to China, he did make the point: 'None of our major products and services are available in China.' He also called artificial intelligence (AI) more profound than fire or electricity, and said privacy is'foundational to everything we do'. Pichai's firm posted whopping revenues of $55.3 billion in the first quarter of this year, but he argued against suggestions it's a'surveillance capitalist'. The Open Internet is a fundamental network (net) neutrality concept.

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.

Retired Navy SEAL Commander Dave Sears suggests Russia, China and Pakistan could face national security issues once U.S. troops leave Afghanistan. This is a rush transcript of "Your World with Neil Cavuto" on July 8, 2021. This copy may not be in its final form and may be updated. QUESTION: Do you trust the Taliban, Mr. President? Do you trust the Taliban, sir? JOE BIDEN, PRESIDENT OF THE UNITED STATES: Are you -- is that a serious question? QUESTION: It is absolutely a serious question. Do you trust the Taliban? BIDEN: No, I do not. BIDEN: No, I do not trust the Taliban. QUESTION: Is the U.S. responsible for the deaths that happen the Afghans after you leave the country? QUESTION: Mr. President, will you amplify that question, please? Will you amplify your answer, please, why you don't trust the Taliban? BIDEN: It is a silly question. Do I trust the Taliban? And it almost seemed like a Donald Trump press conference, with angry reporters trying to get a simple answer from the president, and their agitation showing, as the questions and the nonanswers went on, all of this at a time U.S. forces are moving rapidly ahead of schedule. Better than 90 percent now have left Afghanistan. And we could see them all out well before the 9/11 deadline that the president has set. But he says he's not going to change his mind. And he says that, after 20 years, Afghans must look after themselves. Jennifer Griffin has more from the Pentagon.

Using a modified GAN architecture, they can move objects in the image without affecting the background or the other objects!

As part of the lead-up to Transform 2021 coming up July 12-16, we're excited to put a spotlight on some of our conference speakers who are leading impactful diversity, equity, and inclusion initiatives in AI and data. We were lucky to land a conversation with Huma Abidi, senior director of AI software products and engineering at Intel. She spoke about her DE&I work in her private life, including her support for STEM education for girls in the U.S. and all over the world, founding the Women in Machine Learning group at Intel, and more. HA: This one is easy. I lead a globally diverse team of engineers and technologists responsible for delivering world-class products that enable customers to create AI solutions.

AI-powered job interview software may be just as bullshit as you suspect, according to tests run by the MIT Technology Review's "In Machines We Trust" podcast that found two companies' software gave good marks to someone responding to an English-language interview in German. Companies that advertise software tools powered by machine learning for screening job applicants promise efficiency, effectiveness, fairness, and the elimination of shoddy decision-making by humans. In some cases, all the software does is read resumes or cover letters to quickly determine if an applicant's work experience appears right for the job. But a growing number of tools require job-seekers to navigate a hellish series of tasks before they even come close to a phone interview. These can range from having conversations with a chatbot to submitting to voice/face recognition and predictive analytics algorithms that judge them based on their behavior, tone, and appearance.