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MONTREAL On July 19th, Behavox will host a conference to share the next generation of artificial intelligence in Compliance and Security with clients, regulators, and industry leaders.

The Behavox AI in Compliance and Security Conference will be held at the company HQ in Montreal. With this exclusive in-person conference, Behavox is relaunching its pre-COVID tradition of inviting customers, regulators, AI industry leaders, and partners to its Montreal HQ to deep dive into workshops and keynote speeches on compliance, security, and artificial intelligence.

Were extremely excited to relaunch our tradition of inviting clients to our offices in order to learn directly from the engineers and data scientists behind our groundbreaking innovations, said Chief Customer Intelligence Officer Fahreen Kurji. Attendees at the conference will get to enjoy keynote presentations as well as Innovation Paddocks where you can test drive our latest innovations and also spend time networking with other industry leaders and regulators.

Keynote presentations will cover:

The conference will also feature Innovation Paddocks where guests will be able to learn more from the engineers and data scientists behind Behavox innovations. At this conference, Behavox will demonstrate its revolutionary new product Behavox Quantum. There will be test drives and numerous workshops covering everything from infrastructure for cloud orchestration to the AI engine at the core of Behavox Quantum.

Whats in it for participants?

Behavox Quantum has been rigorously tested and benchmarked against existing solutions in the market and it outperformed competition by at least 3,000x using new AI risk policies, providing a holistic security program to catch malicious, immoral, and illegal actors, eliminating fraud and protecting your digital headquarters.

Attendees at the July 19th conference will include C-suite executives from top global banks, financial institutions, and corporations with many prospects and clients sending entire delegations to the conference. Justin Trudeau, Canadian Prime Minister, will give the commencement speech at the conference in recognition/ celebration of the world leading AI innovations coming out of Canada.

This is a unique opportunity to test drive the product and meet the team behind the innovations as well as network with top industry professionals. Register here for the Behavox AI in Compliance and Security Conference.

About Behavox Ltd.

Behavox provides a suite of security products that help compliance, HR, and security teams protect their company and colleagues from business risks.

Through AI-powered analysis of all corporate communications, including email, instant messaging, voice, and video conferencing platforms, Behavox helps organizations identify illegal, immoral, and malicious behavior in the workplace.

Founded in 2014, Behavox is headquartered in Montreal and has offices in New York City, London, Seattle, Singapore, and Tokyo.

More information about the company is available at https://www.behavox.com/.

View source version on businesswire.com: https://www.businesswire.com/news/home/20220628006051/en/

Contacts

Press: media@behavox.com

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Link:
Deep Dive Into Advanced AI and Machine Learning at The Behavox Artificial Intelligence in Compliance and Security Conference - Financial Post

Deep learning (DL) could be defined as a form of machine learning based on artificial neural networks which harness multiple processing layers in order to extract progressively better and more high-level insights from data. In essence it is simply a more sophisticated application of artificial intelligence (AI) platforms and machine learning (ML).

Here are some of the top trends in deep learning:

Model Scale Up

A lot of the excitement in deep learning right now is centered around scaling up large, relatively general models (now being called foundation models). They are exhibiting surprising capabilities such as generating novel text, images from text, and video from text. Anything that scales up AI models adds yet more capabilities to deep learning. This is showing up in algorithms that go beyond simplistic responses to multi-faceted answers and actions that dig deeper into data, preferences, and potential actions.

Scale Up Limitations

However, not everyone is convinced that the scaling up of neural networks is going to continue to bear fruit. Roadblocks may lie ahead.

There is some debate about how far we can get in terms of aspects of intelligence with scaling alone, said Peter Stone, PhD, Executive Director, Sony AI America.

Current models are limited in several ways, and some of the community is rushing to point those out. It will be interesting to see what capabilities can be achieved with neural networks alone, and what novel methods will be uncovered for combining neural networks with other AI paradigms.

AI and Model Training

AI isnt something you plug in and, presto, instant insights. It takes time for the deep learning platform to analyze data sets, spot patterns, and begin to derive conclusions that have broad applicability in the real world. The good news is that AI platforms are rapidly evolving to keep up with model training demands.

Instead of weeks to learn enough to begin to function, AI platforms are undergoing fundamental innovation, and are rapidly reaching the same maturity level as data analytics. As datasets become larger, deep learning models become more resource-intensive, requiring a lot of processing power to predict, validate, and recalibrate millions of times. Graphics Processing Units (GPUs) are advancing to handle this computing and AI platforms are evolving to keep up with model training demands.

Organizations can enhance their AI platforms by combining open-source projects and commercial technologies, said Bin Fan, VP Open Source and Founding Engineer atAlluxio.

It is essential to consider skills, speed of deployment, the variety of algorithms supported, and the flexibility of the system while making decisions.

Containerized Workloads

Deep learning workloads are increasingly containerized, further supporting autonomous operations, said Fan. Container technologies enable organizations to have isolation, portability, unlimited scalability, and dynamic behavior in MLOps. Thus, AI infrastructure management would become more automated, easier, and more business-friendly than before.

Containerization being the key, Kubernetes will aid cloud-native MLOps in integrating with more mature technologies, said Fan.

To keep up with this trend, organizations can find their AI workloads running on more flexible cloud environments in conjunction with Kubernetes.

Prescriptive Modeling over Predictive Modeling

Modeling has gone through many phases over the last many years. Initial attempts tried to predict trends from historical data. This had some value, but didnt take into account factors such as context, sudden traffic spikes, and shifts in market forces. In particular, real-time data played no real part in early efforts at predictive modeling.

As unstructured data became more important, organizations wanted to mine it to glean insight. Coupled with the rise in processing power, suddenly real time analysis rose to prominence. And the immense amounts of data generated by social media has only added to the need to address real time information.

How does this relate to AI, deep learning, and automation?

Many of the current and previous industry implementations of AI have relied on the AI to inform a human of some anticipated event, who then has the expert knowledge to know what action to take, said Frans Cronje, CEO and Co-founder of DataProphet.

Increasingly, providers are moving to AI that can anticipate a future event and take the correspondent action.

This opens the door to far more effective deep learning networks. With real time data being constantly used by multi-layered neural networks, AI can be utilized to take more and more of the workload away from humans. Instead of referring the decision to a human expert, deep learning can be used to prescribe predicted decisions based on historical, real-time, and analytical data.

Link:
5 Top Deep Learning Trends in 2022 - Datamation

Todays data centers face a challenge that, initially, looks like its almost impossible to resolve. While operations have never been busier, teams are pressured to reduce their facilities energy consumption as part of corporate carbon reduction goals. And, as if that wasnt difficult enough, dramatically rising electricity prices are placing real stress on data center budgets.

With data centers focused on supporting the essential technology services that people increasingly demand to support their personal and professional lives, its not surprising that data center operations have never been busier. Driven by trends that show no sign of slowing down, were seeing massively increased data usage associated with video, storage, compute demands, smart IoT integrations, as well as 5G connectivity rollouts. However, despite these escalating workloads, the unfortunate reality is that many of todays critical facilities simply arent running efficiently enough.

Given that the average data center operates for over 20 years, this shouldnt really be a surprise. Efficiency is invariably tied to a facilitys original design - and based on expected IT loads that have long been overtaken. At the same time change is a constant factor, with platforms, equipment design, topologies, power density requirements and cooling demands all evolving with the continued drive for new applications. The result is a global data center infrastructure that regularly finds it hard to match current and planned IT loads to their critical infrastructure. This will only be exacerbated as data center demands increase, with analyst projections suggesting that workload volumes are set to continue growing at around 20% a year between now and 2025.

Traditional data center approaches are struggling to meet these escalating requirements. Prioritizing availability is largely achieved at efficiencys expense, with too much reliance still placed on operator experience and trusting that assumptions are correct. Unfortunately, the evidence suggests that this model is no longer realistic. EkkoSense research reveals an average figure of 15% of IT racks in data centers operating outside of ASHRAEs temperature and humidity guidelines, and that customers strand up to 60% of their cooling capacity due to inefficiencies. And thats a problem, with Uptime Institute estimating that the global value attributed to inefficient cooling and airflow management is around $18bn. Thats equivalent to some 150bn wasted kilowatt hours.

With 35% of the energy used in a data center utilized to support the cooling infrastructure, its clear that traditional performance optimization approaches are missing a huge opportunity to unlock efficiency improvements. EkkoSense data indicates that a third of unplanned data center outages are triggered by thermal issues. Finding a different way to manage this problem can provide operations teams with a great way to secure both availability and efficiency improvements.

Limitations of traditional monitoringUnfortunately, only around 5% of M&E teams currently monitor and report their data center equipment temperatures on a rack-by-rack basis. Additionally, DCIM and traditional monitoring solutions can provide trend data and be set up to provide alerts when breaches occur, but that is where they stop. They lack the analytics to provide deeper insite into the cause of the issues and how both to resolve them and avoid them in the future.

Operations teams recognize that this kind of traditional monitoring has its limitations, but they also know that they simply dont have the resources and time to take the data they have and convert it from background noise into meaningful actions. The good news is that technology solutions are now available to help data centers tackle this problem.

It's time for data centers to go granular with machine learning and AIThe application of machine learning and AI creates a new paradigm in terms of how to approach data center operations. Instead of being swamped by too much performance data, operations teams can now take advantage of machine learning to gather data at a much more granular level meaning they can start to access how their data center is performing in real-time. The key is to make this accessible, and using smart 3D visualizations is a great way of making it easy for data center teams to interpret performance data at a deeper level: for example, by showing changes and highlighting anomalies.

The next stage is to apply machine learning and AI analytics to provide actionable insights. By augmenting measured datasets with machine learning algorithms, data center teams can immediately benefit from easy-to-understand insights to help support their real-time optimization decisions. The combination of real-time granular data collection every five minutes and AI/machine learning analytics allows operations not just to see what is happening across their critical facilities but also find out why and what exactly they should do about it.

AI and machine learning powered analytics can also uncover the insights required to recommend actionable changes across key areas such as optimum set points, floor grille layouts, cooling unit operation and fan speed adjustments. Thermal analysis will also indicate optimum rack locations. And because AI enables real-time visualizations, data center teams can quickly gain immediate performance feedback on any actioned changes.

Helping data center operations to make an immediate difference Given pressure to reduce carbon consumption and minimize the impact of electricity price increases, data center teams need new levels of optimization support if they are to deliver against their reliability and efficiency goals.

Taking advantage of the latest machine learning and AI-powered data center optimization approaches can certainly make a difference by cutting cooling energy and usage with results achievable within weeks. Bringing granular data to the forefront of their optimization plans, data center teams have already been able to not only remove thermal and power risk, but also secure reductions in cooling energy consumption costs and carbon emmissions by an average of 30%. Its hard to ignore the impact these kind of savings can have particularly during a period of rapid electricity price increases. The days of trading off risk and availability for optimization is a thing of the past with power of AI and Machine learning at the forefront of operating your data center.

Related: Scale Your Machine Learning with MLOps

Want to know more? Register for Wednesday's AFCOMwebinar on the subject here.

About the author

Tracy Collins is Vice President of EkkoSense Americas, the company that enables true M&E capacity planning for power, cooling and space. He was previously CEO at Simple Helix, a leading AL-based Tier III data center operator.

Tracy has over 25 years in-depth data center industry experience, having previously served as Vice President of IT Solutions for Vertiv and, before that, with Emerson Network Power. In his role, Tracy is committed to challenging traditional approaches to data center management particularly in terms of solving the optimization challenge of balancing increased data center workloads while also delivering against corporate energy saving targets.

Read this article:
How AI and Machine Learning Are Ready To Change the Game for Data Center Operations - Data Center Knowledge

LONDON, June 28, 2022 (GLOBE NEWSWIRE) -- According to The Business Research Company's research report on the lawful interception market, leveraging Artificial Intelligence (AI), machine learning, and blockchain technologies for cyber defense is a key trend in the lawful interception market. Lawful interception providers integrate AI and machine learning principles into their solutions to tackle crucial hyper-connected workplace risks with quicker identification, prevention, and responsive capabilities. Advances in technology, such as AI and machine learning, turn the tables on cybercrime. For example, Equifax experienced cyber-attacks which resulted in the loss of sensitive information from more than 140 million American customers. The stolen information included names, addresses, social security numbers, birth dates, and driver's license numbers. Cybersecurity specialists are therefore leveraging AI and machine learning technology to resolve the emerging cyber threats facing individuals, companies, and governments. According to a recent Research and Markets study, the demand for artificial intelligence in cyber security is expected to reach $38.2 billion by 2026.

Request for a sample of the global lawful interception market report

The global lawful interception market size is expected to grow from $2.96 billion in 2021 to $3.57 billion in 2022 at a compound annual growth rate (CAGR) of 20.64%. The growth in the market is mainly due to the companies resuming their operations and adapting to the new normal while recovering from the COVID-19 impact, which had earlier led to restrictive containment measures involving social distancing, remote working, and the closure of commercial activities that resulted in operational challenges. The global lawful interception market share is expected to reach $7.86 billion in 2026 at a CAGR of 21.84%.

The increasing number of cybercrimes is expected to propel the growth of the lawful interception market. Cybercrimes are defined as the increasing number of cyber-attacks through various social media platforms, the internet, and hacking software. The increased cybercrimes are responsible for the growth of lawful interceptions as they are a key tool for identifying crimes. As per the Internet Crime Report 2021, published by the Federal Bureau of Investigation (FBI) in the U.S., there were approximately 791,790 complaints of suspected internet crimean increase of more than 300,000 complaints from 2019. For instance, in 2021, Tessian Research, a data loss prevention (DLP) on email company, found that employees received an average of 14 malicious emails per year. Phishing is the most popular type of cybercrime in which criminals seek to gain sensitive information by sending phone emails or messages. Therefore, increasing cybercrimes drive the lawful interception market.

Major players in the lawful interception market are Utimaco, Vocal Technologies, AQSACOM, Verint, BAE Systems, SS8 Networks, Signalogic, IPS S.P.A, Tracespan, Accuris Networks, EVE Compliancy Solutions, Squire Technologies, Incognito Software, Incognito Software, Net Optics, and Ixia.

The global lawful interception market analysis is segmented by device into mediation devices, routers, intercept access point (IAP), gateways, switches, management servers, others; by network technology into Voice-Over-Internet Protocol (VoIP), Long Term Evolution (LTE), Wireless Local Area Network (WLAN), Worldwide Interoperability for Microwave Access (WiMAX), Digital Subscriber Line (DSL), Public Switched Telephone Network (PSTN), Integrated Services for Digital Network (ISDN), others; by communication content into voice communication, video, text messaging, facsimile, digital pictures, file transfer; by end user into lawful enforcement agencies, government.

North America was the largest region in the lawful interception market in 2021. Asia Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the lawful interception industry report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, the Middle East, and Africa.

Lawful Interception Global Market Report 2022 Market Size, Trends, And Global Forecast 2022-2026 is one of a series of new reports from The Business Research Company that provide lawful interception market overviews, lawful interception market analyze and forecast market size and growth for the whole market, lawful interception market segments and geographies, lawful interception market trends, lawful interception market drivers, lawful interception market restraints, lawful interception market leading competitors' revenues, profiles and market shares in over 1,000 industry reports, covering over 2,500 market segments and 60 geographies.

The report also gives in-depth analysis of the impact of COVID-19 on the market. The reports draw on 150,000 datasets, extensive secondary research, and exclusive insights from interviews with industry leaders. A highly experienced and expert team of analysts and modelers provides market analysis and forecasts. The reports identify top countries and segments for opportunities and strategies based on market trends and leading competitors' approaches.

Not the market you are looking for? Check out some similar market intelligence reports:

Cybersecurity Global Market Report 2022 By Solution (Network Security, Cloud Application Security, End-Point Security, Secure Web Gateway, Internet Security), By Enterprise Size (Small & Medium Enterprise, Large Enterprise), By Deployment Type (Cloud, On Premises), By End-Use (BFSI, IT & Telecommunications, Retail, Healthcare, Government, Manufacturing, Travel And Transportation, Energy And Utilities) Market Size, Trends, And Global Forecast 2022-2026

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Voice Over Internet Protocol (VoIP) Global Market Report 2022 - By Type (Integrated Access Or Session Initiation Protocol (SIP) Trunking, Managed IP PBX, Hosted IP PBX), By Access Type (Phone To Phone, Computer To Computer, Computer To Phone), By Call Type (International VoIP Calls, Domestic VoIP Calls), By Medium (Fixed, Mobile), By End User (Consumers, Small And Medium Businesses, Large Enterprises) - Market Size, Trends, And Global Forecast 2022 2026

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Read more:
Companies In The Lawful Interception Market Are Adopting AI, Machine Learning, And Blockchain Technologie - Benzinga