Category Archives: Machine Learning
Machine Learning and the End of Search – Database Trends and Applications
Video produced by Steve Nathans-Kelly
Does the wider adoption of machine learning mean the end of search as we know it? Northern Light CEO David Seuss explained why he believes it does and how search must leverage smart taxonomies going forward during his presentation at Data Summit Connect 2021.
"We are approaching the era when users will no longer search for information in the traditional way. They will expect the machine to find what they need on its own and bring it to them. To do this, search must evolve to have an in-depth understanding of the search material and the ways of knowing in the user's domain so you can't just throw a generic search solution at a generic content set and have this work," Seuss said.
"The content has got to be about the topic. It's got to be the right content that will have the insights that you're looking for and the search technology from especially the taxonomy viewpoint needs to really be based on an in-depth understanding of the material and the ways of knowing," Seuss explained. To accomplish this, you have to take advantage of smart taxonomies that deeply tag the content with context-specific and meaning-loaded entities, use smart summarization of the important ideas in a document across documents and across sources, and use smart distribution of insightspowered by learning what each user cares about, andthen find the relevant material without being asked.
Instead of searching material, it would be more accurate to say the material finds you, said Seuss."The impact of these analytics and machine learning trends on the socialization and utilization of competitive intelligence in your company will be profound and they will change everything you're doing in your competitive intelligence knowledge management system."
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Machine Learning and the End of Search - Database Trends and Applications
Top Machine Learning Funding and Investments in Q2 2021 – Analytics Insight
From voice assistants to self-driving cars, artificial intelligence and machine learning are overtaking every aspect of the industrial sector. Machine learning algorithms are used to automate laborious tasks in businesses to discover patterns in existing data without being explicitly programmed.
The field is continuously evolving and high-value predictions are being used to make better decisions in real-time without human interventions. Under recent circumstances, investments in machine learning companies have drastically increased. Analytics Insight presents the top machine learning funding and investments in Q2 2021.
Amount Raised: US$15M
Transaction Type: Not Specified
Key Investor(s): Bloomberg, Patrick Collison, and others
Lambda provides deep learning services to top tech companies like Apple, Microsoft, MIT, and others. It is an AI infrastructure providing computation to accelerate human progress.
Amount Raised: US$2M
Transaction Type: Not specified
Key Investor(s): Madrona Venture Group and Jazz Venture Partners
The companys SaaS platform leverages machine learning to discover, analyze, and improve clients business processes without interfering with users or requiring system integrations. Their solutions measure the problems faced by the clients and execute step-by-step processes to solve the issues.
Amount Raised: US$67M
Transaction Type: Series D
Key Investor(s): Chrysalis Investments
The company uses deep learning algorithms to provide cybersecurity services. Deep Instincts on-device solution protects against zero-day threats and APT attacks with unparallel accuracy. By applying deep learning technology, enterprises can gain unmatched protection against cyber threats.
Amount Raised: US$56M
Transaction Type: Series B
Key Investor(s): Tiger Global, Sequoia Capital, and others
Physna is a geometric deep-learning and 3D search company that focuses on comparing and analyzing 3D models. The company powers innovation in manufacturing by bridging the gap between physical objects and digital codes.
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Top Machine Learning Funding and Investments in Q2 2021 - Analytics Insight
KTP Associate NLP & Machine Learning Research Scientist job with UNIVERSITY OF THE WEST OF SCOTLAND | 260243 – Times Higher Education (THE)
University of the West of Scotland in Partnership with Raven Controls Limited
School of Computing, Engineering and Physical SciencesKTP Associate NLP & Machine Learning Research ScientistCompany Based - GlasgowREQ001219Salary up to 34,500 per annum plus 5,000 personal development budgetFull-time: 35 Hours per weekFixed Term: 30 months with potential for permanent employment
University of the West of Scotland (UWS) and Raven Controls are offering an exciting opportunity to a post graduate in Computer Science/Software Engineering/Information Technology or with a relevant degree involving a significant amount of software development to support Raven Controls. You will assist in the development of Raven Controls revolutionary event management. This involves inventing novel methods for NLP, data mining and data analysis using Artificial Intelligence and Machine Learning. You will work as part of the team to enhance the software to be used in the distributed environments. Since we are pioneers in the field, you will help us discover and apply innovative approaches to our existing technology. You should possess excellent communication and interpersonal skills, as well as the ability to work independently and as part of a high-performance team, collaborating with colleagues and sharing expertise. You will be able to demonstrate a proven interest in machine learning and NLP along with a sound knowledge of the C/C++/Python programming language.
The successful candidate will be offered the opportunity to register free of charge for a Higher Degree (Masters or Ph.D.), receive training in Chartered Management Institute (level 5), work with senior company management to realise benefits to the business and apply their degree and lead their own project in a business environment.
Raven Controls Limited was founded based of the MD's ten years experience of working in the Police force in Emergencies and Counter-Terrorism Planning and from his other business ID Resilience - which was formed to test and exercise clients' crisis management plans in a safe and secure environment. Incorporated in May 2017, Raven Controls Limited have developed a digital platform (web-optimised version of their Raven App) to provide real-time situational awareness for all stakeholders involved in event and venue management (particularly for crisis and incident management).
This allows users to stay in control and continuously informed through Raven's issue management system with integrated emergency management principles at its heart.
Find out more by visiting: https://ravencontrols.com/
Given the strategic important of the project, Raven Controls management have indicated their intention to retain the associate post-KTP subject to performance.
About KTP:This position forms part of the Knowledge Transfer Partnership (KTP) funded by Innovate UK. Its essential you understand how KTP works with business and the University, and the vital role you will play if you successfully secure a KTP Associate position. Please visit: http://www.uws.ac.uk/ktp for more information or contact Stuart Mckay at stuart.mckay@uws.ac.uk.
The University of the West of Scotland is committed to supporting your personal development and providing an inclusive working environment. UWS has an Athena SWAN bronze award, is a disability confident employer and welcomes other under-represented groups, as such we particularly encourage applications to support our diversity agenda.
Further information is available by contacting;Professor Naeem Ramzan: naeem.ramzan@uws.ac.uk ; 0141 848 3648 orMr Ian Kerr, Raven Controls: ian@ravencontrols.co.uk
Further information, including details of how to apply are available at https://jobs.uws.ac.uk/
Closing Date: Sunday 15th August 2021Interview Date: Week commencing Monday 30th August 2021
UWS is committed to equality and diversity and welcomes applications from underrepresented groups.
UWS is a Disability Confident employer.
University of the West of Scotland is a registered Scottish charity, no. SC002520.
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KTP Associate NLP & Machine Learning Research Scientist job with UNIVERSITY OF THE WEST OF SCOTLAND | 260243 - Times Higher Education (THE)
AI experts to med students: Don’t compete with the machine. Collaborate with it – AI in Healthcare
Give and take
Some of the liveliest material, including the question and answer above, emerged during a brief Q&A period following the prepared presentations.
To Parikhs point on machine learnings limitations in clinical settings, Chase added an illustrative anecdote.
A couple of medical students told me last week that when the sepsis alert goes off in the electronic health record, basically everybody ignores it because they dont believe it, Chase said. Its black box and was delivered within the electronic health record. Nobodys tested it on sensitivity and specificity.
So [you should] hit the pause button and then decide whether or not [an algorithms] data point applies to your patient.
Demise of the doctors?
Perhaps inevitably, the subject of AIs potential for replacing physicians came up during the Q&A.
Theres no question that imaging-based specialtiesradiology, pathology, dermatologyhave been notably successful using machine learning, Chase responded. But the goal should be better care by way of a happy outcome for both AI and human image interpreters.
I think the next generation of radiologists will be operating at a higher level, Chase explained. Theyll be overseeing the cases that are being referred to them by the machine and making sure that you dont over-biopsy a patient because of a false positive.
And I think that actually will make the profession to some extent more interesting. Youre not going to be looking at film after film that ends up being negative.
Machine, meet patient
Parikh urged attendees to imagine a clinical decision-making scenario from the patients point of view.
If you were hearing that a machine rather than a human was going to be diagnosing your lung cancer would you be interested in that? I would imagine that a lot of patients wouldnt be, Parikh said. Theres still a huge demand for a human element to how we practice medicine. That element is never going to be replaced by machines.
Too often, weve been thinking about these things as adversarialhuman versus machinewhen the real purpose of a machine is to collaborate with a human.
AMA session summary with video here. Standalone video posted to YouTube here.
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AI experts to med students: Don't compete with the machine. Collaborate with it - AI in Healthcare
Feeding the machine: We give an AI some headlines and see what it does – Ars Technica
Enlarge / Turning the lens on ourselves, as it were. Is Our Machine Learning?View more stories
There's a moment in any foray into new technological territory when you realize you may have embarked on a Sisyphean task. Staring at the multitude of options available to take on the project, you research your options, read the documentation, and start to workonly to find that actually just defining the problem may be more work than finding the actual solution.
Reader, this is where I found myself two weeks into this adventure in machine learning. I familiarized myself with the data, the tools, and the known approaches to problems with this kind of data, and I tried several approaches to solving what on the surface seemed to be a simple machine-learning problem: based on past performance, could we predict whether any given Ars headline will be a winner in an A/B test?
Things have not been going particularly well. In fact, as I finished this piece, my most recent attempt showed that our algorithm was about as accurate as a coin flip.
But at least that was a start. And in the process of getting there, I learned a great deal about the data cleansing and pre-processing that goes into any machine-learning project.
Our data source is a log of the outcomes from 5,500-plus headline A/B tests over the past five yearsthat's about as long as Ars has been doing this sort of headline shootout for each story that gets posted. Since we have labels for all this data (that is, we know whether it won or lost its A/B test), this would appear to be a supervised learning problem. All I really needed to do to prepare the data was to make sure it was properly formatted for the model I chose to use to create our algorithm.
I am not a data scientist, so I wasn't going to be building my own model any time this decade. Luckily, AWS provides a number of pre-built models suitable to the task of processing text and designed specifically to work within the confines of the Amazon cloud. There are also third-party models, such as Hugging Face, that can be used within the SageMaker universe. Each model seems to need data fed to it in a particular way.
The choice of the model in this case comes down largely to the approach we'll take to the problem. Initially, I saw two possible approaches to training an algorithm to get a probability of any given headline's success:
The second approach is much more difficult, and there's one overarching concern with either of these methods that makes the second even less tenable: 5,500 tests, with 11,000 headlines, is not a lot of data to work with in the grand AI/ML scheme of things.
So I opted for binary classification for my first attempt, because it seemed the most likely to succeed. It also meant the only data point I needed for each headline (besides the headline itself) is whether it won or lost the A/B test. I took my source data and reformatted it into a comma-separated value file with two columns: titles in one, and "yes" or "no" in the other. I also used a script to remove all the HTML markup from headlines (mostlya few HTML tags for italics). With the data cut down almost all the way to essentials, I uploaded it into SageMaker Studio so I could use Python tools for the rest of the preparation.
Next, I needed to choose the model type and prepare the data. Again, much of data preparation depends on the model type the data will be fed into. Different types of natural language processing models (and problems) require different levels of data preparation.
After that comes tokenization. AWS tech evangelist Julien Simon explains it thusly: Data processing first needs to replace words with tokens, individual tokens. A token is a machine-readable number that stands in for a string of characters. So 'ransomware would be word one, he said, crooks would be word two, setup would be word three so a sentence then becomes a sequence of tokens, and you can feed that to a deep-learning model and let it learn which ones are the good ones, which ones are the bad ones.
Depending on the particular problem, you may want to jettison some of the data. For example, if we were trying to do something like sentiment analysis (that is, determining if a given Ars headline was positive or negative in tone) or grouping headlines by what they were about, I would probably want to trim down the data to the most relevant content by removing "stop words"common words that are important for grammatical structurebut don't tell you what the text is actually saying (like most articles).
However, in this case, the stop words were potentially important parts of the dataafter all, we're looking for structures of headlines that attract attention. So I opted to keep all the words. And in my first attempt at training, I decided to use BlazingText, a text processing model that AWS demonstrates in a similar classification problem to the one we're attempting. BlazingText requires the "label" datathe data that calls out a particular bit of text's classificationto be prefaced with "__label__". And instead of a comma-delimited file, the label data and the text to be processed are put in a single line in a text file, like so:
Another part of data preprocessing for supervised training ML is splitting the data into two sets: one for training the algorithm, and one for validation of its results. The training data set is usually the larger set. Validation data generally is created from around 10 to 20 percent of the total data.
There has been a great deal of research into what is actually the right amount of validation datasome of that research suggests that the sweet spot relates more to the number of parameters in the model being used to create the algorithm rather than the overall size of the data. In this case, given that there was relatively little data to be processed by the model, I figured my validation data would be 10 percent.
In some cases, you might want to hold back another small pool of data to test the algorithm after it's validated. But our plan here is to eventually use live Ars headlines to test, so I skipped that step.
To do my final data preparation, I used a Jupyter notebookan interactive web interface to a Python instanceto turn my two-column CSV into a data structure and process it. Python has some decent data manipulation and data science-specific toolkits that make these tasks fairly straightforward, and I used two in particular here:
Heres a chunk of the code in the notebook that I used to create my training and validation sets from our CSV data:
I started by using pandas to import the data structure from the CSV created from the initially cleaned and formatted data, calling the resulting object "dataset." Using the dataset.head() command gave me a look at the headers for each column that had been brought in from the CSV, along with a peek at some of the data.
The pandas module allowed me to bulk-add the string "__label__" to all the values in the label column as required by BlazingText, and I used a lambda function to process the headlines and force all the words to lower case. Finally, I used the sklearn module to split the data into the two files I would feed to BlazingText.
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Feeding the machine: We give an AI some headlines and see what it does - Ars Technica
Machine Learning is Set to Detect Driver Drowsiness to Reduce Road Accidents – Analytics Insight
The machine learning approach is used for drowsiness detection of drivers to reduce the number of road accidents per year. Integration of machine learning algorithms into computer vision can help to detect whether drivers are feeling drowsy through video streams and facial recognition. IIT Ropar has built an algorithm that can extract facial features of drowsiness like eyes and mouths to effectively detect the real-time feeling of a driver. This is expected to reduce road accidents in a country by alerting the drivers on time.
There are three techniques that the team of IIT Ropar developed drivers operational behavior can be tracked with the understanding of the steering wheel, accelerator or brake patterns and speed; physiological features of a driver like heart rate, head posture or pulse rate and computer vision system to recognize facial expressions. Machine learning can detect drivers drowsiness accurately in multiple vehicle models.
The tech companies and institutes have realized the utmost need for machine learning algorithms in drowsiness detection. Scientists have developed this alert system with the help of Video Stream Processing that analyses an eye blink through an Eye Aspect Ratio (EAR) as well as the Euclidean distance of an eye. IoT can send a warning message with a degree of collision along with real-time location data. The Raspberry Pi, OpenCV or Python monitoring system will help in issuing this crucial message on the spot.
EAR includes a simple calculation that is based on the ratio of distances between the lengths and width of the eyes. The eye aspect is very crucial in detecting drowsiness. Thus, EAR can be plotted for multiple frames of a video sequence through computer vision. There are three command lines to order the detector to use shape-predictor, alarm, and webcam. If the EAR for a driver starts to decline over multiple frames, the machine learning algorithms can detect that the driver is drowsy. There is also a presence of Mouth Aspect Ratio (MAR) the ratio of distances between the length and width of the mouth of a driver. This will detect when the driver will yawn and lose control over the mouth. There is a significant emphasis on the pupil of the eye known as Pupil Circularity. It helps to detect whether the eyes are half-open or almost closed during driving.
Thus, the advancement in cutting-edge technology is utilized in reducing road accidents per year with the help of machine learning algorithms. It is a natural feeling to be drowsy on roads for numerous causes. Thus, it is the work of machine learning algorithms to protect drivers and their families from incurring a massive loss.
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SBH Health System Selects ElectrifAi’s Machine Learning Technology to Transform Operations – KPVI News 6
JERSEY CITY, N.J., July 8, 2021 /PRNewswire/ -- ElectrifAi, one of the world's leading companies in practical artificial intelligence (AI) and pre-built machine learning (ML) models, announced today its collaboration with St. Barnabas Hospital, the flagship of the SBH Health System, a teaching institution caring for an underserved population in the Bronx.
The collaboration will rapidly bring SBH operational efficiencies, cost savings, spending control, increased revenue and risk reduction. SBH will leverage ElectrifAi's pre-built machine learning models for spend, contract, revenue capture, claim denials, patient engagement and leakage, and many other applications.
SBH can now leverage vendor terms, uncover inappropriate charges, missed discounts and additional findings by transforming contracts into a strategic digital asset. SBH will receive detailed views of spend by doctor, physicians group, facility and other fields as discovered through spend analytics from various data sources. SBH will also be able to audit identified missed charges for billing and re-bill opportunities by uncovering missed charges from a vendor's revenue cycle management system.
ElectrifAi's 17 years of practical machine learning expertise with spend analytics, contract management, customer/patient engagement and machine learning models devoted to patient claims denials can help optimize and improve the operations of SBH and make SBH the trailblazer of the greater Tri-State medical community.
"For years, our customers in financial services, telecom and retail have improved their business processes though our practical machine learning technology. Now our clients in healthcare are benefitting from our integrated pre-built machine learning models tailored to the business of running hospital systems more cost-effectively," said Ed Scott, CEO of ElectrifAi. "Now, SBH Health System and its peers can accelerate implementation of machine learning to drive revenue uplift, reduce costs, increase profit and improve general performance amid a fast-changing business environment. We are thrilled about our collaboration with SBH," Scott added.
"We are very excited about our collaboration with ElectrifAi. The challenges of running a hospital system efficiently in today's fast-paced world can be daunting; but through the help of ElectrifAi's leading-edge practical machine learning models, we look forward to rapidly implementing operational efficiencies that will help us keep pace and continue to serve our patients and community at the highest levels," says Dr. Eric Appelbaum, Chief Medical Officer of SBH.
About ElectrifAi
ElectrifAi is a global leader in business-ready machine learning models. ElectrifAi's mission is to help organizations change the way they work through machine learning: driving revenue uplift, cost reduction as well as profit and performance improvement. Founded in 2004, ElectrifAi boasts seasoned industry leadership, a global team of domain experts, and a proven record of transforming structured and unstructured data at scale. A large library of Ai-based products reaches across business functions, data systems, and teams to drive superior results in record time. ElectrifAi has approximately 200 data scientists, software engineers and employees with a proven record of dealing with over 2,000 customer implementations, mostly for Fortune 500 companies. At the heart of ElectrifAi's mission is a commitment to making Ai and machine learning more understandable, practical and profitable for businesses and industries across the globe. ElectrifAi is headquartered in New Jersey, with offices located in Shanghai and New Delhi. To learn more visitwww.electrifAi.netand follow us on Twitter@ElectrifAiand onLinkedIn.
About SBH Health System
St. Barnabas Hospital is the flagship of the SBH Health System, a teaching institution which cares for an underserved population in the Bronx. A major provider of ambulatory care services, with more than 200,000 outpatient visits annually, the 422-bed hospital includes a Level II trauma center, a stroke center and a hemodialysis center. SBH is also a major provider of behavioral health services through its various programs designed to support and meet the mental health needs of adults, teens and children in the borough.
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SBH Health System Selects ElectrifAi's Machine Learning Technology to Transform Operations - KPVI News 6
Qeexo and STMicroelectronics Speed Development of Next-Gen IoT Applications with Machine-Learning Capable Motion Sensors – EE Journal
Mountain View, CA and Geneva, Switzerland, July 7, 2021Qeexo, developer of the Qeexo AutoML automated machine-learning (ML) platform that accelerates the development of tinyML models for the Edge, andSTMicroelectronics (NYSE: STM),a global semiconductor leader serving customers across the spectrum of electronics applications,today announced the availability of STs machine-learning core (MLC) sensors on Qeexo AutoML.
By themselves, STs MLC sensors substantially reduce overall system power consumption by running sensing-related algorithms, built from large sets of sensed data, that would otherwise run on the host processor. Using this sensor data, Qeexo AutoML can automatically generate highly optimized machine-learning solutions for Edge devices, with ultra-low latency, ultra-low power consumption, and an incredibly small memory footprint. These algorithmic solutions overcome die-size-imposed limits to computation power and memory size, with efficient machine-learning models for the sensors that extend system battery life.
Delivering on the promise we made recently when we announced our collaboration with ST, Qeexo has added support for STs family of machine-learning core sensors on Qeexo AutoML,said Sang Won Lee, CEO of Qeexo.Our work with ST has now enabled application developers to quickly build and deploy machine-learning algorithms on STs MLC sensors without consuming MCU cycles and system resources, for an unlimited range of applications, including industrial and IoT use cases.
Adapting Qeexo AutoML for STs machine-learning core sensors makes it easier for developers to quickly add embedded machine learning to their very-low-power applications,said Simone Ferri, MEMS Sensors Division Director, STMicroelectronics.Putting MLC in our sensors, including the LSM6DSOX or ISM330DHCX, significantly reduces system data transfer volumes, offloads network processing, and potentially cuts system power consumption by orders of magnitude while delivering enhanced event detection, wake-up logic, and real-time Edge computing.
About Qeexo
Qeexo is the first company to automate end-to-end machine learning for embedded edge devices (Cortex M0-M4 class). Our one-click, fully-automated Qeexo AutoML platform allows customers to leverage sensor data to rapidly build machine learning solutions for highly constrained environments with applications in industrial, IoT, wearables, automotive, mobile, and more. Over 300 million devices worldwide are equipped with AI built on Qeexo AutoML. Delivering high performance, solutions built with Qeexo AutoML are optimized to have ultra-low latency, ultra-low power consumption, and an incredibly small memory footprint. For more information, go tohttp://www.qeexo.com.
About STMicroelectronics
At ST, we are 46,000 creators and makers of semiconductor technologies mastering the semiconductor supply chain with state-of-the-art manufacturing facilities. An independent device manufacturer, we work with more than 100,000 customers and thousands of partners to design and build products, solutions, and ecosystems that address their challenges and opportunities, and the need to support a more sustainable world. Our technologies enable smarter mobility, more efficient power and energy management, and the wide-scale deployment of the Internet of Things and 5G technology. Further information can be found atwww.st.com.
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Trust Swiftly Launches 15 Verification Method Platform with Machine Learning to Increase E-commerce Fraud Prevention – Yahoo Finance
Identity Verification Company Trust Swiftly focuses on providing companies a customizable verification package that keeps authenticated users in the fast lane, while requiring high-risk users further checks to defeat multiple fraud attacks
MILWAUKEE, WI / ACCESSWIRE / July 8, 2021 / Trust Swiftly launches the first-ever identity verification platform featuring 15 different methods of authentication that safely approves real e-commerce customers while stopping fraudsters fast.
By combining multiple verifications, Trust Swiftly provides legitimate customers the most efficient and enjoyable experience possible while fraudulent actors are quickly identified. The platform is customizable and allows users the capability to feature as many of the verification methods as they see fit.
This package allows companies to treat each customer uniquely in a pay-as-you-go pricing package without lengthy contracts. In addition, to the extensive verification methods, the Trust Swiftly system allows clients to store their data in over 22 regions worldwide which creates a high level of privacy as Trust Swiftly is not collecting any unnecessary information from their customers' database.
According to Digital Commerce 360 analysis of U.S. Department of Commerce Data, 2021's Q1 e-commerce shopping spiked to nearly 20 percent compared to 7.6 percent in 2012. The speed at which e-commerce shopping is growing shows not only the capability of companies to get products and services to customers efficiently, but the increasing trust customers have gained doing so online.
'As our capabilities increase in delivering goods and services online, so does the expertise of fraudulent actors looking to infiltrate businesses,' said Patrick Scanlan, co-founder and CEO of Trust Swiftly. 'Not only have fraud actors become more sophisticated, but they continue to advance. Our machine learning system tracks hundreds of distinct attributes from each verification and can identify the fraudulent patterns automatically and in turn prevent declines and loss growth due to fraud.'
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In fact, according to a Juniper Research report, e-commerce retailers are at risk of losing more than $20 billion in 2021 due to fraud. Trust Swiftly beta clients saw their fraud rates drop by 40 percent, with one client seeing a $15,000 per month return on investment by easily authenticating customers and stopping repeat fraud.
The 15 methods of verification include options like phone SMS ownership, credit card ownership, ID ownership, selfie liveness, document ownership and geolocation to name a few. Trust Swiftly's technology accurately detects irregularities and provides a central and dynamic platform to verify users no matter the attacks faced.
With global coverage without security compromises and compliance with privacy regulations, Trust Swiftly is quick and easy to set up with no additional coding required to integrate with the suite of applications. For more information, please visit Trust Swiftly.
About Trust Swiftly
Trust Swiftly accurately detects fraudulent identities using a dynamic set of verification methods and machine learning so businesses can trust their customers and grow faster. Its privacy-first platform and flexible pricing allow companies to integrate identity verification into multiple business processes.
Trust Swiftly was founded in 2021 and is headquartered in Milwaukee, WI and available in 100+ countries and seven different languages. For more information about the suite of applications visit trustswiftly.com.
Media Contact
Company: Trust SwiftlyContact: Andrew WilliamsTelephone: +1 312-945-0121Email: andrew@trustswiftly.comWebsite: https://trustswiftly.com/
SOURCE: Trust Swiftly
View source version on accesswire.com: https://www.accesswire.com/654739/Trust-Swiftly-Launches-15-Verification-Method-Platform-with-Machine-Learning-to-Increase-E-commerce-Fraud-Prevention
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Trust Swiftly Launches 15 Verification Method Platform with Machine Learning to Increase E-commerce Fraud Prevention - Yahoo Finance
Boosting IT Security with AI-driven SIEM – IT Business Edge
Employing SIEM (security information and event management) software provides the enterprise with threat monitoring, event correlation, incident response, and reporting. SIEM collects, centralizes, and analyzes log data through enterprise technology, including applications, firewalls, and other systems. It subsequently alerts your IT security team of failed logins, malware, and other potentially malicious activities.
However, over the years, SIEM has barely evolved beyond the ability to provide a better, more searchable rule-based log engine. The marriage of recent Artificial Intelligence (AI) and Machine Learning (ML) technologies with cybersecurity tools promises a glorious future.
In 2016, Gartner coined another new term, Artificial Intelligence for IT operations, or AIOps. AI and machine learning-based algorithms coupled with predictive analytics are quickly becoming a core part of SIEM platforms. These platforms provide automated, continuous analysis and correlation of all activity observed within a given IT environment. This integration lends SIEM with deep learning capabilities and a myriad of integrated tools to drive more informed results.
Following are the benefits of such an integrated SIEM.
Also read: AIOps Trends & Benefits for 2021
A typical SIEMs analytics correlates events from different sources gathered over a relatively short period (typically hours and days). This, when compared with an infrastructures baseline, will output a prioritized alert if they exceed the preset thresholds. AIOps represent systems that store event information gathered over a long period (perhaps years) in a database and then apply analytics to that data.
Such analytics enables AIOps to adjust the infrastructure baseline and adjust alerting thresholds over time, as well as automatically undertake some remedial actions based on correlated events. In addition, employing big data lends SIEM the ability to detect even the very slow or stealth activities on a network that SIEM would otherwise miss or dismiss as a one-off. By detecting these slow or stealth activities, a security team can prevent a major security incident.
Besides offering standard log data, AI and machine learning technologies can also incorporate threat intelligence feeds. Some products can also feature advanced security analytics capabilities that look at both user and network behavior. Machine learning enables your SIEM to facilitate threat detection across large data sets, alleviating some threat hunting responsibilities from your security team. Threat intelligence provides insights into the likely intent of individual IP addresses, websites, domains, and other entities on the internet. This allows them to distinguish a normal activity from a malicious one.
Providing your SIEM with continuous access to one or multiple threat intelligence feeds enables machine learning technologies to use the context that the threat intelligence delivers. And as it learns more, it starts to understand malicious behavior warnings beyond its initial data input. Therefore, it can stop threats your cybersecurity has never seen before. It improves the SIEMs decision-making, particularly in terms of accuracy, thus helping to deepen your security layers.
There is a caveat, though. Machine learning works better on larger datasets than smaller ones, but because big data is lossy, it may complicate compliance reporting. But as this is a known problem, there are multiple workaround options available.
Also read: What is SIEM Software and How Can It Protect Your Company?
A typical SIEM provides a considerable amount of monitoring data/logs, but SIEM report data is not actionable, hard to understand, and contains too much noise. An AI integrated SIEM solution manages big data efficiently and can replace repetitive, redundant tasks with automated workflows.
Although most AI programs facilitate data classification, the AI element isnt capable of grouping unrecognizable data points and event information. On the other hand, machine learning can leverage data clustering capabilities to identify these unknown values and group them into categories based on similarities detected.
As an enterprise scales up, it becomes more susceptible to blind spots appearing. And each blind spot can go unmonitored for months, if not for years at a time. Consequently, these parts of the network can go unpatched for long periods of time. These blind spots further become a perfect place of infiltration for the hackers to plant dwelling threats.
Fortunately, AI in SIEM can help improve the visibility of your network, thus quickly and periodically uncovering blind spots in your networks. It can also draw security logs from these recently uncovered blind spots, in turn expanding the reach of your SIEM solution.
Also read: Steps to Improving Your Data Architecture
The Security Operation Center (SOC) teams of any enterprise are limited, and the amount of log data generated from any SIEM is quite considerable. This makes the challenge of dealing with incidents in a responsive and effective manner extremely daunting. More so, a lot of SIEM tools also provide a lot of unrelated data, causing the SOC teams to face alert fatigue.
This situation happens when dealing with too many alerts and not knowing which alerts you should pay attention to and ignore. Automated and standardized workflows provided by ML can reduce the possibility of human error and get the job done much quicker.
SIEM also requires constant monitoring from your IT security team. Manually monitoring every system checkpoint is not only exhausting but will also induce burnout. SIEM backed with ML capabilities can offer:
Unfortunately, SIEM backed by simple machine learning capabilities cannot match the power of human ingenuity and collective collaboration of cybersecurity adversaries. Hence, the enterprises security team needs to take the lead on threat hunting and incident response.
However, a properly implemented AI-augmented SIEM can optimize these processes through its predictive and automated capabilities. Such SIEM can provide the groundwork for your IT security team:
Essentially, you can think of this technology not only as a second pair of eyes, but also another set of hands. However, keep in mind that specialized human intelligence will always triumph over AI.
Machine learning algorithms augment SIEM systems, enabling them to use previous patterns to predict and anticipate future data.
For example, consider the data patterns provided during a security breach. Machine learning capabilities enable systems to internalize those patterns and then use them to detect suspicious activities that could show a subsequent breach or infiltration.
An AI-augmented SIEM can halt processes they suspect to be malicious. Not only can this help with investigations and threat remediation, but it also mitigates damage even before incident response begins.
For relatively small companies or those with simple IT infrastructure, the cost of an AI-enabled SIEM would probably be prohibitive while offering little to no advantage when coupled with good security hygiene. A large and complex IT infrastructure might justify the cost of an AI-enabled SIEM for an enterprise. However, it is always advisable to get a detailed evaluation of the products.
Gartner predicts that by 2023, $175.5B will be spent on information security and risk management. And, data security, cloud security, and infrastructure protection are the fastest-growing areas of security spending through 2023. In 2018, a whopping $7.1B was spent on AI-based cybersecurity systems and services, which is predicted to reach $30.9B in 2025, according to Zion Market Research.
As the world generates more and more data in an increasingly digital marketplace, the security of your organizations critical information is of the utmost importance. Threat intelligence-enabled cybersecurity tools will become the most valuable asset for your company as cyberattacks grow in sophistication and frequency.
Read next: Best Practices for Application Security
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Boosting IT Security with AI-driven SIEM - IT Business Edge