AIOps 6 Things to Avoid When Selecting a Solution – insideBIGDATA

In this special guest feature, Paul Scully, a Vice President at Grok, believes that sometimes its easier to look at what NOT to do in order to find an AIOps solution that will work for your company. Read on to learn more about what to avoid when it comes to finding an AIOps platform that will benefit your company. With 20 years of deep expertise in helping IT Organizations improve the reliability and efficiency of their infrastructure, Grok is intently focused on building the industrys most innovative platform to bring the best of Machine Learning to IT Operations Management.

As data grows, so, too, does the AIOps market. Forrester reports 68 percent of companies surveyed have plans to invest in AIOps-enabled monitoring solutions over the next 12 months. And Gartner estimates the size of the AIOps platform market at between $300 million and $500 million per year. It poses the question if you are going to spend millions on AIOps platforms and integrate them into your critical systems, how do you know what to look for?

Sometimes its easier to look at what NOT to do in order to find a solution that will work for your company. Read on to learn more about what to avoid when it comes to finding an AIOps platform that will benefit your company.

AVOID: Significant Retooling of Your Current Platforms

If you are looking for significant short-term benefits from an AIOps platform for IT Operations Management (ITOM), you should be wary of solutions that require replacing large portions of your current systems. Organizations that take a throw the baby out with the bathwater approach to implementing AIOps find themselves bogged down with too much to do because these projects focus on replacing much of the existing toolset. In reality, this approach only increases the complexity, cost, and timing of deploying machine learning in IT Operations.

Most ITOM systems have evolved over many years, with significant effort already invested to ingest and format data, and then integrate the data with other systems. Similarly, the work queues have also evolved to incorporate a deep knowledge of the event handling process and/or incident management process. Replacing these functions only complicates the adoption of AIOps. You should consider AIOps platforms that can easily integrate into your existing monitoring infrastructure, adding an intelligence layer to the existing footprint. This allows for a much faster deployment time as well as focusing the effort and work on what really matters: results.

AVOID: Locking into a Single ITOM Reference Architecture

There are many AIOPs platforms on the market that are extensions of existing product portfolios. These solutions typically only have good integrations with tools inside their portfolio but tend to discourage integrating outside of the ecosystem if that means replacing one of their existing solutions. This makes it difficult to replace these systems or augment them with best of breed point solutions.

When evaluating an AIOps solution customers should consider solutions that are not beholden to a single vendors ecosystem. A solution that is truly agnostic provides much more flexibility and reduced total cost of ownership over time. Think twice about AIOps platforms that:

AVOID: Approaches That Require Frequent Re-Training

Different AIOPs platforms have different requirements. Different requirements mean your teams have to be trained certain ways. Understanding the objectives of the AIOps platform is important up front since they define what the data focuses on and how the operations team will work with them.

For instance, AIOps platforms that are focused on Service Assurance need to be real-time, are required to scale and must respond in seconds. This type of solution is deployed in an environment where resources are already stretched thin, meaning teams do not have the skill set to conduct constant care and feeding of the platform (nor do they have the time to frequently retrain the algorithms). Make sure youre looking for an offering that does not require constant manual retraining and that can easily integrate different data feeds.

AVOID: Offerings with a Singular Focus

Many AIOps offerings actually only focused on a single area of artificial intelligence and ingest a single data type. For example, there are countless offerings that are focused on applying machine learning to log data while others are focused on time series data and others events. To be a complete AIOps solution for Service Assurance requires the ability to ingest Logs, Events and Performance Metrics all of them, not just one. Also, remember that this ingestion of data needs to be done against real-time, streamingdata not only historical data.

AVOID: Marketing Messages as Cover for Lack of AI

The term AI has a very broad definition, whereas the term Machine Learning is more focused, and Deep Learning even more so. However, these terms have somehow become interchangeable. They are not. Unfortunately, some vendors have capitalized on the AI boom by adding AI to their marketing messages or by adding a very small amount of AI functionality to their existing offering so they can claim their solutions is an AIOps platform. This is misleading at best and deceptive at worst.

One way you can spot deceptive marketing messages is if the offering requires a lot of manual rules. True Machine Learning solutions should not require a long list of rules be built and maintained to implement the solution. Furthermore, pay attention to the types of machine learning algorithms that are deployed in the solution. If there is only one type of algorithm such as limited anomaly detection then chances are the solution has added a minimal amount of AI capability in an attempt to put marketing ahead of technology capabilities.

AVOID: Platforms that Dont Adequately Scale

Scalability is important, especially for AI systems that have strict time constraints. AIOps systems that run against primary historical data for the purpose analytics tend have less constraints on response times from the machine learning models. However, if the system is focused on real-time data such as in a Service Assurance environment response time becomes very important.

As the business grows so does the data within the organization. When new customers are brought on, they come with new data and potentially new equipment. As new services are rolled out new data is generated and all of this new data must be captured in the AI platform. Sizing the AI platform at the beginning for the data set that exists at the time can quickly result in the system running out of resources causing response time degradation or worse system failure.

Deploying AI within a microservices architecture allows for components to more easily scale on demand. In addition, it allows components to be decentralized and scaled at the component layer versus across all components.

Knowing what to avoid when implementing AIOps is just as important as knowing what to look for. At the end of the day you want a robust platform that operates with various types of data, that does not require significant retooling of your architecture or continual retaining the algorithms, and that can scale as your data increases. Keep focused on the objectives you want to accomplish with an AIOps platform and insist on real technology, not marketing messages or limited add-ons. These real solutions exist and, once implemented, can make a considerable contribution to your Operations team.

Sign up for the free insideBIGDATAnewsletter.

View post:
AIOps 6 Things to Avoid When Selecting a Solution - insideBIGDATA

Scality Invests to Advance AI and Machine Learning with Inria Research Institute – HPCwire

SAN FRANCISCO, Calif., June 16, 2020 Scality, provider of software solutions for global data orchestration and distributed file and object storage, announced an investment in Fondation Inria, the Foundation of the well-known French national research institute for digital sciences,Inria. Bringing both financial and collaboration backing to the institute, Scality will help support multi-disciplinary research and innovation initiatives. This includes mind-body health, precision agriculture, neurodegenerative diagnostics, privacy protection and more.

To be at the forefront of technological advancements and research has been a priority for Scality since our inception and we currently hold 10 patents. It only made sense for us to deepen our relationship with one of the most advanced research institutes on AI and algorithms in the world, said Jrme Lecat, Scality CEO and co-founder. We believe that technology and digital sciences can provide answers to the issues facing our fractured global society. Inria research teams work on incredible projects that actually change lives with personalized medicine, precision agriculture, sustainable development, smart cities and mobility, and security and privacy protection.

Scality has been close to Inria for many years and is involved with several collaborative research projects that are developing new concepts for distributed and scalable storage with Inria Distinguished Research Scholar,Marc ShapiroOne such project isRainbowFSwhich investigates an approach to distributed storage that ensures distributed consistency semantics tailored to applications in order to develop smarter and massively scalable systems.

We are delighted to be working with Scality. This collaboration is bringing two major players in French technology closer in order to further research and innovation on a global scale, said Jean-Baptiste Hennequin, Fondation Inria managing director. Our values align very closely to Scalitys: innovative research, social responsibility and open source. For example, our sheltered foundations are promoting the distribution of open source software for the durable development by bringing together their user communities within consortia, in recognition of how software embodies humanitys technical and scientific knowledge.

Read more about some of the exciting projects carried out by Inria research teams:

Originally posted here:
Scality Invests to Advance AI and Machine Learning with Inria Research Institute - HPCwire

After Effects and Premiere Pro gain more ‘magic’ machine-learning-based features – Digital Arts

ByNeil Bennett| on June 16, 2020

Roto Brush 2 (above) makes masking easier in After Effects, while Premiere Rush and Pro will automatically reframe and detect scenes in videos.

Adobe has announced new features coming to its video post-production apps, on the date when it was supposed to be holding its Adobe Max Europe event in Lisbon, which was cancelled due to COVID-19.

These aren't available yet unlike the new updates to Photoshop, Illustrator and InDesign but are destined in future releases. We would usually expect these to coincide with the IBC conference in Amsterdam in September or Adobe Max in October, though both of these are virtual events this year.

The new tools are based on Adobe's Sensei machine-learning technology. Premiere Pro will gain the ability to identify cuts in a video and create timelines with cuts or markers from them ideal if you've deleted a project and only have the final output, or are working with archive material.

A second-generation version of After Effects' Roto Brush enables you to automatically extract subjects from their background. You paint over the subject in a reference frame and the tech tracks the person or object through a scene to extract them.

Premiere Rush will be gaining Premiere Pro's Auto Reframe feature, which identify's key areas of video and frames around them when changing aspect ratio for example when creating a square version of video for Instagram or Facebook.

Also migrating to Rush from Pro will be an Effects panel, transitions and Pan and Zoom.

Note: We may earn a commission when you buy through links on our site, at no extra cost to you. This doesn't affect our editorial independence. Learn more.

Go here to read the rest:
After Effects and Premiere Pro gain more 'magic' machine-learning-based features - Digital Arts

AI: The complex solution to simplify health care – Brookings Institution

Health care languishes in data dissonance. A fundamental imbalance between collection and use persists across systems and geopolitical boundaries. Data collection has been an all-consuming effort with good intent but insufficient results in turning data into action. After a strong decade, the sentiment is that the data is inconsistent, messy, and untrustworthy. The most advanced health systems in the world remain confused by what theyve amassed: reams of data without a clear path toward impact. Artificial intelligence (AI) can see through the murk, clear away the noise, and find meaning in existing data beyond the capacity of any human(s) or other technology.

AI is a term for technologies or machines that have the capability to adapt and learn. This is the fundamental meaning of being data-driven, to be able to take measure of available data and perform an action or change ones mind. Machine Learning is at the heart of AIteaching machines to learn from data, rather than requiring hard-coded rules (as did machines of the past).

No domain is more deserving of meaningful AI than health care. Health care is arguably the most complex industry on earthoperating at the nexus of evolving science, business, politics, and mercurial human behavior. These influences push and pull in perpetual contradiction.

Health carespecifically psychologyis the mother of machine learning. In 1949, Dr. Donald Hebb created a model of brain cell interactions, or synaptic plasticity, that forms the ancestral architecture of the artificial neural networks that pervade AI today. Math to explain human behavior became mathematics to mimic and transcend human intellect. AI is now at the precipice of a return to the health care domain.

To achieve impact at scale, machine learning must be deployed in the most and least advanced health systems in the world. Any decent technology should remain resilient outside the walls of academia and the pristine data environments of tech giants. AI can learn from many dimensions of dataphotographs, natural language, tabular data, satellite imageryand can adapt, learning from the data thats available. The ability to adapt is what defines AI. AI at its best is designed to solve complex problemsnot wardrobe preferences. Now is the time to bring AI to health care.

COVID-19 is the greatest global crisis of our time: an immediate health challenge and a challenge of yet unknown duration on the economic and psychological well-being of our society. The lack of data-driven decisionmaking and the absence of adaptive and predictive technology have prolonged and exacerbated the toll of COVID-19. It will be the adoption of these technologies that helps us to rebuild health and society. AI has already forged new solutions for the COVID-19 response and the accelerated evolution of health care. Machine learning models from MIT for transmission rates have generated impressive precisionin some cases reducing error rates by 70 percent. Researchers at Mount Sanai in New York City have demonstrated the ability to reduce testing from two days to near instant by combining AI models with chest computed tomography (CT), clinical symptoms, exposure history, and laboratory testingreducing error of false negatives. AI modelsunlike test kitscan travel instantly to new users, are not limited in production, and do not require additional training and complementary equipment.

Adoption of AI must be done in concert with existing systems and solutions. Epidemiological models in concert with AI technology adapt and learn in real timeintegrating new data to help explain ancillary elements of health outcomes. However, collaboration between epidemiology and machine learning has been limited. The prominent epidemiological models are not integrating dynamic machine learning. Without machine learning, epidemiological models are updated weekly, losing precious time and rendering wildly inaccurate predictions that have been widely criticized. Human bias is writ large in these modelsvariable importance is determined by experts rather than learned and derived from the data.

AI models can derive implicit and explicit features from available data to increase the precision and adaptability of transmission predictions. Organizations like Metabiota have mapped thousands of pandemics to generate a model for risk. Existing electronic information systems (EIS) hold valuable historical health data when they are availableboth pandemic models and EIS are excellent sources for AI engines targeted at optimization of pandemic response at scale.

Optimizationin terms of tuning a health system to produce a maximum value (life expectancy, for example) or minimum value (cost of care) is the end goal of AI for health. By looking forward into the future and predicting demand, constraints, and behavior, AI can buy time. Time to prepare and ensure that resources are deployed to maximize the impact of every unit: financial, human, or commodity. Most models look backwardslike driving a car by only looking at the rearview mirroryet they are asked to make decisions for the future. Its Sisyphean to ask legacy analytics to prepare for tomorrow based on what is often a distant (months, weeks, or days at best) past of linear data inputs. Optimization through machine learning and AI technologies brings the prescience to data-driven decisions and actions required for impact. Machine-learning-optimized laboratory testing at MIT has accelerated discovery of new antibiotics previously considered unachievable due to the significant time and financial investment.

At the health system level, action is being accelerated through direct engagement with those at the front lines. Human-in- the-loop (HIL) machine learning (ML) is the process of receiving data-rich insights from people, analyzing them in real time, and sharing recommendations back. HIL ML is the science of teaching machines to learn directly from human input. In Mozambique and slated to expand to Sierra Leone, macro-eyes technology is learning directly from front-line health workersthe foremost experts on the conditions for care in the communities they serve. This becomes a virtuous cycle of high-value data, timely insights, and accelerated engagement at the point of care. Facility-level precision from HIL ML in Sierra Leone will complement AI optimization engines being deployed to probabilistically estimate the availability of essential resources at facilities across the country, account for new resources constraints, and recommend distribution of resources.

COVID-19 has highlighted the need for rapid connection between data analytics and the front lines of care. That connection still does not exist at scale. The result: Authorities must decipher a myriad of models estimating COVID-19-related transmissions and deaths in the near past and estimations for the future that dont build knowledge or data from the ground up. This fundamental disconnect has hindered health care for decadesthose who deliver the care have the least voice in how care is delivered. It can be resolved with minimal disruption using HIL ML to engage an educated and impassioned community of health workers.

AI in health has been successful but far too limited. The inability to trust what we dont fully understand, misrepresentation of AI expertise by early participants, and the financial fortitude of the global funding mechanisms remain barriers to adoption. AI canand willexponentially improve the delivery of care around the world. The data and the data infrastructure are ready and the time for bold investment is now. Investment must move away from pilots with insufficient horizon and commitment. AI at scaleas bold innovations of the pastwill only be possible with a committed corpus of financiers, policymakers, and implementing partners dedicating resources to AI experts solving problems at the foundations of health.

But we must proceed with caution. The world is replete with AI solutions and experts purporting to save the planet. Be criticalthere is very little real AI talent, and even fewer teams have the chops to deploy AI in the real world. The AI scientists of the future will not look like those of the recent past. The software engineers turned AI experts who brought AI to the digital world in Silicon Valley, and academics building models in protected vaults, will be usurped by adaptive, scrappy, problemsolving engineers using AI to make change in the communities they care about: deploying in the physical world meaningful solutions to complex problems. What is more meaningful than health?

Visit link:
AI: The complex solution to simplify health care - Brookings Institution

AI Machine Learning Market: Competitive and Regional Market Analysis till 2030 – Cole of Duty

Prophecy Market Insights AI Machine Learning market research report focuses on the market structure and various factors affecting the growth of the market. The research study encompasses an evaluation of the market, including growth rate, current scenario, and volume inflation prospects, based on DROT and Porters Five Forces analyses. The market study pitches light on the various factors that are projected to impact the overall market dynamics of the AI Machine Learning market over the forecast period (2019-2029).

The data and information required in the market report are taken from various sources such as websites, annual reports of the companies, journals, and others and were validated by the industry experts. The facts and data are represented in the AI Machine Learning report using diagrams, graphs, pie charts, and other clear representations to enhance the visual representation and easy understanding the facts mentioned in the report.

Get Sample Copy of This Report @ https://www.prophecymarketinsights.com/market_insight/Insight/request-sample/3249

The AI Machine Learning research study contains 100+ market data Tables, Pie Chat, Graphs & Figures spread through Pages and easy to understand detailed analysis. The predictions mentioned in the market report have been derived using proven research techniques, assumptions and methodologies. This AI Machine Learning market report states the overview, historical data along with size, share, growth, demand, and revenue of the global industry.

All the key players mentioned in the AI Machine Learning market report are elaborated thoroughly based on R&D developments, distribution channels, industrial penetration, manufacturing processes, and revenue. Also, the report examines, legal policies, and competitive analysis between the leading and emerging and upcoming market trends.

AI Machine LearningMarket Key Companies:

Segmentation Overview:

Global AI machine learning market by type:

Global AI machine learning market by application:

Global AI machine learning market by region:

Apart from key players analysis provoking business-related decisions that are usually backed by prevalent market conditions, we also do substantial analysis on market segmentation. The report provides an in-depth analysis of the AI Machine Learning market segments. It highlights the latest trending segment and major innovations in the market. In addition to this, it states the impact of these segments on the growth of the market.

Request [emailprotected] https://www.prophecymarketinsights.com/market_insight/Insight/request-discount/3249

Regional Overview:

The survey report includes a vast investigation of the geographical scene of the AI Machine Learning market, which is manifestly arranged into the localities. The report provides an analysis of regional market players operating in the specific market and outcomes related to the target market for more than 20 countries.

Australia, New Zealand, Rest of Asia-Pacific

Key Questions Answered in Report:

Stakeholders Benefit:

About us:

Prophecy Market Insights is specialized market research, analytics, marketing/business strategy, and solutions that offers strategic and tactical support to clients for making well-informed business decisions and to identify and achieve high-value opportunities in the target business area. We also help our clients to address business challenges and provide the best possible solutions to overcome them and transform their business.

Contact Us:

Mr. Alex (Sales Manager)

Prophecy Market Insights

Phone: +1 860 531 2701

Email: [emailprotected]

Link:
AI Machine Learning Market: Competitive and Regional Market Analysis till 2030 - Cole of Duty

Unpack the use of AI in cybersecurity, plus pros and cons – TechTarget

AI is under the spotlight as industries worldwide begin to investigate how the technology will help them improve their operations.

AI is far from being new. As a field of scientific research, AI has been around since the 1950s. The financial industry has been using a form of AI -- dubbed expert systems -- for more than 30 years to trade stocks, make risk decisions and manage portfolios.

Each of these use cases exploits expert systems to process large amounts of data quickly at levels that far exceed the ability of humans to perform the same tasks. For instance, algorithmic stock trading systems make millions of trades per day with no human interaction.

Cybersecurity seeks to use AI and its close cousin, machine learning -- where algorithms that analyze data become better through experience -- in much the same way that the financial services industry has.

For cybersecurity professionals, that means using AI to take data feeds from potentially dozens of sources, analyze each of these inputs simultaneously in real time and then detect those behaviors that may indicate a security risk.

Beyond the use of AI and machine learning in cybersecurity risk identification, these technologies can be used to improve access control beyond the weak username and password systems in widespread use today by including support for multifactor, behavior-based, real-time access decisions. Other applications for AI include spam detection, phishing detection and malware detection.

Today's networked environments are extremely complex. Monitoring network performance is challenging enough; detecting unwanted behavior that may indicate a security threat is even more difficult.

Traditional incident response models are based on a three-pronged concept: protect, detect and respond. Cybersecurity experts have long known that of the three, detect is the weak link. Detection is hard to do and is often not done well.

In 2016, Gartner unveiled its own predict, prevent, detect and respond framework that CISOs could use to communicate a security strategy. Machine learning is particularly useful in predicting, preventing and detecting.

There are enormous amounts of data that must be analyzed to understand network behavior. The integration of machine learning and the use of AI in cybersecurity tools will not just illuminate security threats that previously may have gone undetected, but will help enterprises diagnose and respond to incursions more effectively.

AI-based security algorithms can identify malicious behavior patterns in the huge volumes of network traffic far better than people can. However, this technology can only identify the behavioral patterns the algorithms have been trained to identify. With machine learning, AI can go beyond the limits of algorithms and automatically improve its performance through learning or experience. The ability for AI -- and machine learning in particular -- to make decisions based upon data rather than rules promises to yield significant improvements in detection.

Let's examine how the integration of AI and machine learning might help improve the performance of intrusion detection and prevention systems (IDSes/IPSes). A typical IDS/IPS relies upon detection rules, known as signatures, to identify potential intrusions, policy violations and other issues.

The IDS/IPS looks for traffic that matches the installed signatures. But the IDS/IPS can identify malicious traffic only if a signature matching that malicious traffic is installed: no signature, no detection. This means the IDS/IPS cannot detect attacks whose signatures have yet to be developed. In addition, a signature-based IDS/IPS may also be easy to circumvent by making small changes to attacks so that they avoid matching a signature.

To close this gap, IDSes/IPSes have for years employed something called heuristic anomaly detection. This lets systems look for behavior that is out of the ordinary, as well as attempt to classify anomalous traffic as either benign, suspicious or unknown. When suspicious or unknown traffic is flagged, these systems generate an alert, which requires a human operator to determine whether the threat is malicious. But IDSes/IPSes are hobbled by the sheer volume of data to be analyzed, the number of alerts generated and especially the large percentage of false positives. As a result, signature-based IDSes/IPSes dominate.

The use of malicious AI, also known as adversarial AI, is growing.

One way to help the heuristic IDS/IPS become more efficient would be the introduction of machine learning-generated probability scores that determine which activity is benign and which is harmful.

The challenge, however, is that, of the billions of actions that occur on networks, relatively few of them are malicious. It is kind of a double-edged sword: There is too much data for humans to process manually and too little malicious activity for machine learning tools to learn effectively on their own.

To address this issue, security analysts train machine learning systems by manually labeling and classifying potential anomalies in a process called supervised learning. Once a machine learning cybersecurity system learns about an attack, it can search for other instances that reflect the same or similar behavior. This method may feel like it's nothing more than automating the discovery and creation of attack signatures, but the knowledge an machine learning system learns about attacks can be applied in a far more comprehensive approach than traditional signature detection systems can muster.

That's because machine learning systems can look for and identify behavior that is similar or related to what it has learned rather than rigidly focus on behavior that exactly matches a traditional signature.

The use of AI in cybersecurity offers the possibility of using technology to cut through the complexity of monitoring current networks, thus improving risk and threat detection. However, the use of AI in cybersecurity is a two-way street. The use of malicious AI, also known as adversarial AI, is growing. A malicious actor could potentially use AI to make a series of small changes to a network environment that, while individually insignificant, could change the overall behavior of the machine learning cybersecurity system once they are integrated over time.

This adversarial AI security threat is not limited to AI used in cybersecurity. It is a potential threat wherever AI is used, including in common industrial control systems and computer vision systems used in banking applications.

This means the AI models themselves are becoming a new attack surface component that must be secured. New security practices will have to be adopted. Some protection strategies will look like things we already know how to do, such as rate-limiting inputs and input validation.

Over time, AI adversarial training could be included as part of the supervised learning process. The uses and benefits of AI and machine learning in cybersecurity are real and necessary. There is too much data to process. It can take months to detect intrusions in today's large network data sets. AI can help detect malicious traffic, but it will take significant effort to develop and train an effective AI cybersecurity system. And, as is the case with all technology, AI can also be deployed maliciously. Mitigating the impact of malicious AI is also a reality in today's security environment.

More:
Unpack the use of AI in cybersecurity, plus pros and cons - TechTarget

CVPR 2020 Convenes Thousands from the Global AI, Machine Learning and Computer Vision Community in Virtual Event Beginning Sunday – thepress.net

LOS ALAMITOS, Calif., June 12, 2020 /PRNewswire/ --The Computer Vision and Pattern Recognition (CVPR) Conference, one of the largest events exploring artificial intelligence, machine learning, computer vision, deep learning, and more, will take place 14-19 June as a fully virtual event. Over the course of six days, the event will feature 45 sessions delivered by 1467 leading authors, academics, and experts to more than 6500 attendees, who have already registered for the event.

"The excitement, enthusiasm, and support for CVPR from the global community has never been more apparent," said Professor of Computer Science at Cornell University and Co-Chair of the CVPR 2020 Committee Ramin Zabih. "With large attendance, state of the art research, and insights delivered by some of the leading authorities in computer vision, AI, and machine learning, our first-ever fully virtual event is shaping up to be an exciting experience for everyone involved."

As a fully virtual event, attendees will have access to all CVPR program components, including fireside chats, workshops, tutorials, and oral and poster presentations via a robust, fully searchable, password-protected portal. Credentials to access the portal are provided to attendees shortly upon registration.

CVPR fireside chats, workshops, and tutorials will be conducted via live video with live Q&A between presenters and participants. Oral and poster presentations, which will be repeated, will include a pre-recorded video from the presenter(s), followed by a live Q&A session. Attendees will also be able to access presentations/papers and the pre-recorded videos at their convenience to help ensure maximum access given the diverse time zones in which conference participants live. Additionally, CVPR participants can leverage complementary video chat features and threaded question and answer commenting associated with each session and each sponsor to support further knowledge sharing and understanding. Multiple online networking events with video and text chat elements are also included.

"The CVPR Committee has gone to great lengths to deliver a first-in-class virtual conference experience that all attendees can enjoy," said IEEE Computer Society Executive Director Melissa Russell, co-sponsor of the event. "We are thrilled to be part of this endeavor and are excited to deliver and witness in the coming days the 'what's next' in AI, computer vision and machine learning."

Details on the full virtual CVPR 2020 schedule can be found on the conference website at http://cvpr2020.thecvf.com/program. All times are Pacific Daylight Time (Seattle Time).

Interested individuals can still register for CVPR at http://cvpr2020.thecvf.com/attend/registration. Accredited members of the media can register for the CVPR virtual conference by emailing media@computer.org.

About CVPR 2020CVPR is the premier annual computer vision and pattern recognition conference. With first-in-class technical content, a main program, tutorials, workshops, a leading-edge expo, and attended by more than 9,000 people annually, CVPR creates a one-of-a-kind opportunity for networking, recruiting, inspiration, and motivation. CVPR 2020, originally scheduled to take place 14-19 June 2020 at the Washington State Convention Center in Seattle Washington, will now be a fully virtual event. Authors and presenters will virtually deliver presentations and engage in live Q&A with attendees. For more information about CVPR 2020, the program, and how to participate virtually, visit http://cvpr2020.thecvf.com/.

About the Computer Vision FoundationThe Computer Vision Foundation is a non-profit organization whose purpose is to foster and support research on all aspects of computer vision. Together with the IEEE Computer Society, it co-sponsors the two largest computer vision conferences, CVPR and the International Conference on Computer Vision (ICCV).

About the IEEE Computer SocietyThe IEEE Computer Society is the world's home for computer science, engineering, and technology. A global leader in providing access to computer science research, analysis, and information, the IEEE Computer Society offers a comprehensive array of unmatched products, services, and opportunities for individuals at all stages of their professional career. Known as the premier organization that empowers the people who drive technology, the IEEE Computer Society offers international conferences, peer-reviewed publications, a unique digital library, and training programs. Visit http://www.computer.org for more information.

View original post here:
CVPR 2020 Convenes Thousands from the Global AI, Machine Learning and Computer Vision Community in Virtual Event Beginning Sunday - thepress.net

Research Associate / Postdoc – Machine Learning for Computer Vision job with TECHNISCHE UNIVERSITAT DRESDEN (TU DRESDEN) | 210323 – Times Higher…

At TU Dresden, Faculty of Computer Science, Institute of Artificial Intelligence, the Chair of Machine Learning for Computer Vision offers a position as

Research Associate / Postdoc

Machine Learning for Computer Vision

(subject to personal qualification employees are remunerated according to salary group E 14 TV-L)

starting at the next possible date. The position is limited for three years with the option of an extension. The period of employment is governed by the Fixed Term Research Contracts Act (Wissenschaftszeitvertragsgesetz - WissZeitVG). The position aims at obtaining further academic qualification. Balancing family and career is an important issue. The post is basically suitable for candidates seeking part-time employment. Please note this in your application.

Tasks:

Requirements:

Applications from women are particularly welcome. The same applies to people with disabilities.

Please submit your comprehensive application including the usual documents (CV, degree certificates, transcript of records, etc.) by 31.07.2020 (stamped arrival date of the university central mail service applies) preferably via the TU Dresden SecureMail Portal https://securemail.tu-dresden.de/ by sending it as a single PDF document to mlcv@tu-dresden.de or to: TU Dresden, Fakultt Informatik, Institut fr Knstliche Intelligenz, Professur fr Maschinelles Lernen fr Computer Vision, Herrn Prof. Dr. rer. nat. Bjrn Andres, Helmholtzstr. 10, 01069 Dresden. Please submit copies only, as your application will not be returned to you. Expenses incurred in attending interviews cannot be reimbursed.

Reference to data protection: Your data protection rights, the purpose for which your data will be processed, as well as further information about data protection is available to you on the website: https: //tu-dresden.de/karriere/datenschutzhinweis

Please find the german version under: https://tu-dresden.de/stellenausschreibung/7713.

See the original post here:
Research Associate / Postdoc - Machine Learning for Computer Vision job with TECHNISCHE UNIVERSITAT DRESDEN (TU DRESDEN) | 210323 - Times Higher...

Tamr: Machine Learning Can Be Used to Transform Creative Talent Management – Media & Entertainment Services Alliance M&E Daily Newsletter

Machine learning can be used by the best talent managers today to transform creative talent management and find the right opportunities for their clients, according to Matt Holzapfel, solutions lead at enterprise data unification and data mastering specialist Tamr.

In an industry that runs on storytelling, its stories are increasingly informed by huge amounts of data: hundreds of datasets, millions of records and billions of data points (including tweets) from sources inside and outside the business. By using machine learning to serve up analytics-ready data from disparate data, creative talent management firms can create very human stories with mutually successful outcomes for clients and media companies time and time again.

Tamr helps large organizations clean up dirty data so that they can get that data ready for their analytic and digital transformation aspirations, Holzapfel said during a May 27 presentation at the Hollywood Innovation and Transformation Summit (HITS) Liveevent.

During the presentation Using Machine Learning to Transform Creative Talent Management, he explained how Tamr helped Creative Artists Agency specifically use machine learning to take a new lens to what the data management ecosystem should look like in order to transform how they were using data and analytics within the company.

In the process, Tamr was able to dramatically increase the throughput of their analytics and help drive more insight for their agents, he said.

Within every industry, the old saying is your biggest assets leave in the elevator every night, he noted, adding: Within entertainment, nothing is more true in that people are the entertainment industrys biggest asset. The actors, the musicians, the artists that people pay to see [are] really at the heart of the entertainment industry.

And he pointed out that one of the biggest challenges within the industry is how you match the right talents, the right piece of content for the right audience.

It is often not the end analytic that is the most challenging part, he told viewers, explaining: I think in a lot of cases, when were talking about data, were usually thinking about those analytics: the visualization, the model whatever it is that comes out the other end that helps us make a decision. However, what often is the biggest bottleneck is the data around it, he said.

As an example, he noted that we can look at actor Vin Diesel and try to gauge his social reach, the top demographics that include his fans and what an ideal role for him would be where a company could attract a big audience and be successful.

If the data is readily available at our fingertips and nicely organized, then these questions become pretty quick to answer, he said, adding: We can answer these questions in seconds. But often today they take weeks [to answer] because the data itself is not neatly organized. If we want to understand who is Vin Diesels target market [and] what roles should we put him in, that involves pulling audience data, YouTube data, social media data about what are people talking about [and] what the sentiment is like.

Some of that data is structured and some of it is unstructured, he noted. But the bottom line is that its extremely buried and scattered everywhere and so it makes it difficult to even have the information needed in order to make decisions confidently, he told viewers.

At the end of the day, any decision within this industry is a bit of a leap of faith, but without the data to back it up, youre often just kind of flying blind, he said.

Once you get the data organized in a warehouse, the next problem that companies face is the data itself is dirty, he noted.

If you want to figure out the impact of, for example, Steve Carell on the TV show The Office, you have to sift through all of this data, and just wrangling and organizing all this data is often the bottleneck for such analytics, he said.

That was a key part of the bottleneck at CAA no matter how much data they were acquiring, they were just running into more and more issues with actually making the data usable, he told viewers.

However, the good news is that, particularly over the past handful or so years, the tools that are available the solutions in the market have evolved quite a bit and we now have what we need in order to solve this problem, he stressed.

Traditionally, the way the market has looked at this problem has been kind of twofold: You have your source system in which you just collect all the data you need so everything is in a warehouse or data lake, and then you need people who can analyze all that data and figure it out, he noted.

The problem with that, however, is many of those analysts, who are very scarce and difficult to come by, end up spending a lot of their time doing one-off cleanup and data preparation, and not on analytics, he pointed out. These kinds of human-intensive approaches are difficult to maintain and lead to poor productivity, he said.

However, what used to take weeks to gain insight now takes only minutes because companies are starting to see their data as an asset and are focused on the data engineering, enabling the prep to be done upstream, he told viewers. That is dramatically reducing the amount of time analysts and data scientists are spending preparing and getting the data right, he said.

And CAA is one of the best examples that weve seen in the media and entertainment industry of reducing the time to insight from two weeks down to two seconds, he noted.

He went on to stress: There isnt one silver bullet to solving this problem. There isnt a single suite or a single solution that you can buy thats going to do everything that you need to do in order to solve this problem.

Fortunately, CAA recognized early that it would need to invest in next-generation tools that are open and interoperable and enable you to have that agility to do it, he said. Also important was its shift to modern, cloud-based tools, he said, adding CAA took a completely cloud-first approach to the challenge.

Clickherefor the presentation slide deck.

The May 27 HITS Live event tackled the quickly shifting IT needs of studios, networks and media service providers, along with how M&E vendors are stepping up to meet those needs. The all-live, virtual, global conference allowed for real-time Q&A, one-on-one chats with other attendees, and more.

HITS Live was presented by Microsoft Azure, with sponsorship by RSG Media, Signiant, Tape Ark, Whip Media Group, Zendesk, Eluvio, Sony, Avanade, 5th Kind, Tamr, EIDR and the Trusted Partner Network (TPN). The event is produced by the Media & Entertainment Services Alliance (MESA) and the Hollywood IT Society (HITS), in association with the Content Delivery & Security Association (CDSA) and the Smart Content Council.

For more information, clickhere.

View original post here:
Tamr: Machine Learning Can Be Used to Transform Creative Talent Management - Media & Entertainment Services Alliance M&E Daily Newsletter

Artificial Intelligence and Machine Learning Market Growth Prospects, Revenue, Key Vendors, Growth Rate and Forecast To 2026 – Jewish Life News

Artificial Intelligence and Machine Learning Market Overview

The Artificial Intelligence and Machine Learning market report presents a detailed evaluation of the market. The report focuses on providing a holistic overview with a forecast period of the report extending from 2018 to 2026. The Artificial Intelligence and Machine Learning market report includes analysis in terms of both quantitative and qualitative data, taking into factors such as Product pricing, Product penetration, Country GDP, movement of parent market & child markets, End application industries, etc. The report is defined by bifurcating various parts of the market into segments which provide an understanding of different aspects of the market.

The overall report is divided into the following primary sections: segments, market outlook, competitive landscape and company profiles. The segments cover various aspects of the market, from the trends that are affecting the market to major market players, in turn providing a well-rounded assessment of the market. In terms of the market outlook section, the report provides a study of the major market dynamics that are playing a substantial role in the market. The market outlook section is further categorized into sections; drivers, restraints, opportunities and challenges. The drivers and restraints cover the internal factors of the market whereas opportunities and challenges are the external factors that are affecting the market. The market outlook section also comprises Porters Five Forces analysis (which explains buyers bargaining power, suppliers bargaining power, threat of new entrants, threat of substitutes, and degree of competition in the Artificial Intelligence and Machine Learning) in addition to the market dynamics.

Get Sample Copy with TOC of the Report to understand the structure of the complete report @ https://www.marketresearchintellect.com/download-sample/?rid=292536&utm_source=JLN&utm_medium=888

Leading Artificial Intelligence and Machine Learning manufacturers/companies operating at both regional and global levels:

Artificial Intelligence and Machine Learning Market Scope Of The Report

This report offers past, present as well as future analysis and estimates for the Artificial Intelligence and Machine Learning market. The market estimates that are provided in the report are calculated through an exhaustive research methodology. The research methodology that is adopted involves multiple channels of research, chiefly primary interviews, secondary research and subject matter expert advice. The market estimates are calculated on the basis of the degree of impact of the current market dynamics along with various economic, social and political factors on the Artificial Intelligence and Machine Learning market. Both positive as well as negative changes to the market are taken into consideration for the market estimates.

Artificial Intelligence and Machine Learning Market Competitive Landscape & Company Profiles

The competitive landscape and company profile chapters of the market report are dedicated to the major players in the Artificial Intelligence and Machine Learning market. An evaluation of these market players through their product benchmarking, key developments and financial statements sheds a light into the overall market evaluation. The company profile section also includes a SWOT analysis (top three companies) of these players. In addition, the companies that are provided in this section can be customized according to the clients requirements.

To get Incredible Discounts on this Premium Report, Click Here @ https://www.marketresearchintellect.com/ask-for-discount/?rid=292536&utm_source=JLN&utm_medium=888

Artificial Intelligence and Machine Learning Market Research Methodology

The research methodology adopted for the analysis of the market involves the consolidation of various research considerations such as subject matter expert advice, primary and secondary research. Primary research involves the extraction of information through various aspects such as numerous telephonic interviews, industry experts, questionnaires and in some cases face-to-face interactions. Primary interviews are usually carried out on a continuous basis with industry experts in order to acquire a topical understanding of the market as well as to be able to substantiate the existing analysis of the data.

Subject matter expertise involves the validation of the key research findings that were attained from primary and secondary research. The subject matter experts that are consulted have extensive experience in the market research industry and the specific requirements of the clients are reviewed by the experts to check for completion of the market study. Secondary research used for the Artificial Intelligence and Machine Learning market report includes sources such as press releases, company annual reports, and research papers that are related to the industry. Other sources can include government websites, industry magazines and associations for gathering more meticulous data. These multiple channels of research help to find as well as substantiate research findings.

Table of Content

1 Introduction of Artificial Intelligence and Machine Learning Market

1.1 Overview of the Market1.2 Scope of Report1.3 Assumptions

2 Executive Summary

3 Research Methodology of Verified Market Research

3.1 Data Mining3.2 Validation3.3 Primary Interviews3.4 List of Data Sources

4 Artificial Intelligence and Machine Learning Market Outlook

4.1 Overview4.2 Market Dynamics4.2.1 Drivers4.2.2 Restraints4.2.3 Opportunities4.3 Porters Five Force Model4.4 Value Chain Analysis

5 Artificial Intelligence and Machine Learning Market, By Deployment Model

5.1 Overview

6 Artificial Intelligence and Machine Learning Market, By Solution

6.1 Overview

7 Artificial Intelligence and Machine Learning Market, By Vertical

7.1 Overview

8 Artificial Intelligence and Machine Learning Market, By Geography

8.1 Overview8.2 North America8.2.1 U.S.8.2.2 Canada8.2.3 Mexico8.3 Europe8.3.1 Germany8.3.2 U.K.8.3.3 France8.3.4 Rest of Europe8.4 Asia Pacific8.4.1 China8.4.2 Japan8.4.3 India8.4.4 Rest of Asia Pacific8.5 Rest of the World8.5.1 Latin America8.5.2 Middle East

9 Artificial Intelligence and Machine Learning Market Competitive Landscape

9.1 Overview9.2 Company Market Ranking9.3 Key Development Strategies

10 Company Profiles

10.1.1 Overview10.1.2 Financial Performance10.1.3 Product Outlook10.1.4 Key Developments

11 Appendix

11.1 Related Research

Customized Research Report Using Corporate Email Id @ https://www.marketresearchintellect.com/need-customization/?rid=292536&utm_source=JLN&utm_medium=888

About Us:

Market Research Intellect provides syndicated and customized research reports to clients from various industries and organizations with the aim of delivering functional expertise. We provide reports for all industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverage and more. These reports deliver an in-depth study of the market with industry analysis, market value for regions and countries and trends that are pertinent to the industry.

Contact Us:

Mr. Steven Fernandes

Market Research Intellect

New Jersey ( USA )

Tel: +1-650-781-4080

Our Trending Reports

Arachidonic Acid Market Size, Growth Analysis, Opportunities, Business Outlook and Forecast to 2026

Arc Detector Market Size, Growth Analysis, Opportunities, Business Outlook and Forecast to 2026

Arc Flash Protection System Market Size, Growth Analysis, Opportunities, Business Outlook and Forecast to 2026

Go here to see the original:
Artificial Intelligence and Machine Learning Market Growth Prospects, Revenue, Key Vendors, Growth Rate and Forecast To 2026 - Jewish Life News