The risks of cybersecurity are more complex than ever. Due to the rise of the Internet of Things (IoT) and Artificial Intelligence (AI), by 2020 every person will generate 1.7 megabytes of information per second. As new technologies evolve, cyber criminals adapt and discover new hacking methods to apprehend sensitive data. AI and IoT have the potential to revolutionise society, but what happens when these new technologies are weaponized by cyber criminals? Unless hardware-based endpoint security solutions are implemented in IoT and AI devices, users leave themselves vulnerable to cyberattacks.

Developed between 1966 and 72 at Stanford Research Institute (SRI), wobbly-gaited Shakey, the first robot to reason about its own actions, marked the beginning of artificial intelligence. Half a century later, Hanson Robotics introduced Sophia, a robot who grabbed headlines both for her spontaneity while being interviewed and her status as the first robot to be awarded citizenship. In the fifty years between the activation of these two robots, not only did technology advance but the goals of artificially intelligent machines also changed dramatically. In doing so, we may have made life more convenient in some ways, and in other ways may have paved the way for our own replacements. As robot technology improved, designers moved from physically powerful machines that freed up muscle and might to machines that could work alongside and with people.

This paper studies structured sparse training of CNNs with a gradual pruning technique that leads to fixed, sparse weight matrices after a set number of epochs. We simplify the structure of the enforced sparsity so that it reduces overhead caused by regularization. The proposed training methodology Campfire explores pruning at granularities within a convolutional kernel and filter. We study various tradeoffs with respect to pruning duration, level of sparsity, and learning rate configuration. We show that our method creates a sparse version of ResNet-50 and ResNet-50 v1.5 on full ImageNet while remaining within a negligible <1% margin of accuracy loss. To ensure that this type of sparse training does not harm the robustness of the network, we also demonstrate how the network behaves in the presence of adversarial attacks. Our results show that with 70% target sparsity, over 75% top-1 accuracy is achievable.

New technologies like artificial intelligence and machine learning are changing the way work gets done all over the world. We believe that 2020 is the year that companies will embrace these powerful technologies and apply them to revolutionize their business processes. Here's how process-minded leaders can capture the opportunity. In the past few years, Process Mining grew faster than any other technology in the BPM and process excellence space -- even faster than RPA, according to theInternational Data Corporation, IDC. In 2020, the rapid growth will continue.

Traditional cybersecurity tools such as mere anti-malware software or login audits aren't going to be sufficient in 2020--additional resources will be needed to protect organizations and their employees from cyberthreats. Artificial intelligence (AI) and machine learning (ML) are making productive inroads in the cybersecurity space. I spoke with Anish Joshi, vice president of technology at AI solutions provider Fusemachines, and Greg Martin, general manager of the Security Business Unit at Sumo Logic, a machine data analytics organization to get their input on the topic. The interviews have been lightly edited. Scott Matteson: What are the common pain points with cybersecurity?

For the past five years Chinese tech titan Tencent has published content produced by automated software called Dreamwriter, with a focus on business and financial stories. In 2018, an online platform operated by a company called Shanghai Yingxun Technology Company replicated an AI-generated financial report from Tencent on its own website. While the defendant had already removed the article from its own website, it was still required to pay a fine of 1,500 yuan ($217). AI has increasingly infiltrated most industries in recent years, and the creative arts are no different. Google has created an AI-powered tool called AutoDraw, for example, that identifies what someone is trying to draw and then recreates it in digital form.

The War Powers Resolution passed Thursday by the House in an effort to restrict President Trump's ability to take military action to defend America against attack from Iran is a meaningless political document designed only to embarrass the president. The nonbinding resolution criticizes the president for not "consulting" with Congress and receiving its "authorization" before ordering the killing of Iranian general and terrorist mastermind Qassem Soleimani. The resolution also orders Trump to stop using military force against Iran until he gets congressional approval. Trump acted fully within his constitutional authority when he ordered the drone strike against Soleimani, a mass murderer responsible for thousands of deaths – including over 600 Americans – who was engaged in planning additional imminent and ongoing deadly attacks. President Trump told Fox News' Laura Ingraham in an exclusive interview Friday that Soleimani was planning attacks on four U.S. embassies.

For some time, American companies including Microsoft, Google, and IBM have established research labs in China to tap into local AI talent and to keep track of technological trends. Now, as tensions and restrictions continue to ramp up, some observers wonder if the days of those outposts may be numbered. The US Commerce Department imposed new export controls on artificial intelligence software last week, a measure apparently designed to prevent US companies from shipping AI technology that could train Chinese military drones or teach intelligence systems to interpret aerial imagery. But it remains to be seen how broadly the rules are interpreted, and they are unlikely to be the last to land on American AI algorithms, datasets, and chips. The Commerce Department is, in fact, still weighing further AI software controls, and the Trump administration is increasingly scrutinizing how Silicon Valley interacts with China.

We see the AI space poised for an acceleration in adoption, driven by more sophisticated AI models being put in production, specialized hardware that increases AI's capacity to provide quicker results based on larger datasets, simplified tools that democratize access to the entire AI stack, small tools that enables AI on nearly any device, and cloud access to AI tools that allow access to AI resources from anywhere. Integrating data from many sources, complex business and logic challenges, and competitive incentives to make data more useful all combine to elevate AI and automation technologies from optional to required. And AI processes have unique capabilities that can address an increasingly diverse array of automation tasks, tasks that defy what traditional procedural logic and programming can handle--for example: image recognition, summarization, labeling, complex monitoring, and response. Get a free trial today and find answers on the fly, or master something new and useful. In fact, in our 2019 surveys, more than half of the respondents said AI (deep learning, specifically) will be part of their future projects and products--and a majority of companies are starting to adopt machine learning.

Cyber-attack Automated Unconventional Sensor Environment (CAUSE), applies AI/ML-based models to develop novel, automated methods for event-based detection and prediction of cyber-attacks significantly earlier than existing approaches. Forecasting cyber-attack events with actionable details advances the state-of-the-art by enabling threat-specific cyber incident response and defense measures; Creation of Operationally Realistic 3D Environment (CORE3D), uses machine learning and deep learning techniques to develop methods for the construction of a fully automated high fidelity 3D model of the world using remote sensing data; Deep Intermodal Video Analytics (DIVA), leverages machine learning techniques to develop robust automatic activity detection in streaming video across multiple cameras; Finding Engineering-Linked Indicators (FELIX), uses AI for detection of engineering signatures across multiple biological organisms. The goal is to distinguish natural organisms from those that have been engineered; Functional Map of the World Challenge, developed algorithms that would quickly and accurately classify 63 classes of buildings and regions in satellite imagery. All the top participants used various forms of deep learning; Functional Genomic and Computational Assessment of Threats (Fun GCAT), develops AI/ML-based approaches to learn and classify genetic (e.g., DNA) sequence data by genetic taxonomy, sequence function, and threat potential; Mercury Challenge, asked challenge participants to make use of AI/ML approaches to forecast a variety of political events in the Middle East and North Africa region, such as non-violent civil unrest and military activity; Machine Intelligence from Cortical Networks (MICrONS), aims to revolutionize machine learning by reverse-engineering the algorithms of the brain. The program is expressly designed as a dialogue between data science and neuroscience; Machine Translation for English Retrieval of Information in Any Language (MATERIAL), develops machine learning methods to identify foreign language information from speech and text relevant to English queries, and providing evidence of relevance of the retrieved information in English in a meaningful way.