Daily Archives: October 19, 2022

Every year at Adobe Max, Adobe shows off what it calls Sneaks, R&D projects that might or might not find their way into commercial products someday. This year is no exception, and lucky for us, we were given a preview ahead of the conference proper.

Project Clever Composites (as Adobes calling it) leverages AI for automatic image compositing. To be more specific, it automatically predicts an objects scale, determining where the best place might be to insert it in an image before normalizing the objects colors, estimating the lighting conditions and generating shadows in line with the images aesthetic.

Heres how Adobe describes it:

Image composting lets you add yourself in to make it look like you were there. Or maybe you want to create a photo of yourself camping under a starry sky but only have images of the starry sky and yourself camping during the daytime.

Im no Photoshop wizard, but Adobe tells me that compositing can be a heavily manual, tedious and time-consuming process. Normally, it involves finding a suitable image of an object or subject, carefully cutting the object or subject out of said image and editing its color, tone, scale and shadows to match its appearance with the rest of the scene into which its being pasted. Adobes prototype does away with this.

We developed a more intelligent and automated technique for image object compositing with a new compositing-aware search technology, Zhifei Zhang, an Adobe research engineer on the project, told TechCrunch via email. Our compositing-aware search technology uses multiple deep learning models and millions of data points to determine semantic segmentation, compositing-aware search, scale-location prediction for object compositing, color and tone harmonization, lighting estimation, shadow generation and others.

Image Credits: Adobe

According to Zhang, each of the models powering the image-compositing system is trained independently for a specific task, like searching for objects consistent with a given image in terms of geometry and semantics. The system also leverages a separate, AI-based auto-compositing pipeline that takes care of predicting an objects scale and location for compositing, tone normalization, lighting condition estimation and synthesizing shadows.

The result is a workflow that allows users to composite objects with just a few clicks, Zhang claims.

Achieving automatic object compositing is challenging, as there are several components of the process that need to be composed. Our technology serves as the glue as it allows all these components to work together, Zhang said.

As with all Sneaks, the system could forever remain a tech demo. But Zhang, who believes itd make a great addition to Photoshop and Lightroom, says work is already underway on an improved version that supports compositing 3D objects, not just 2D.

We aim to make this common but difficult task of achieving realistic and clever composites for 2D and 3D completely drag-and-drop, Zhang said. This will be a game-changer for image compositing, as it makes it easier for those who work on image design and editing to create realistic images since they will now be able to search for an object to add, carefully cut out that object and edit the color, tone or scale of it with just a few clicks.

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Adobes AI prototype pastes objects into photos while adding realistic lighting and shadows - TechCrunch

Yuichiro Chino | Moment | Getty Images

Businesses and consumers have been grappling with supply chain issues for months, resulting in annoying shortages of all kinds of products, including all-important semiconductor chips.

And while the CHIPS and Science Act, signed into law in August, is designed to boost semiconductor manufacturing in the U.S., there's no telling what effect the legislation will have on supply, or even when.

"The semiconductor supply chain is still constrained," said Brandon Kulik, semiconductor industry leader and principal at Deloitte Consulting. "Lead times on average have come down slightly, given softening in the consumer electronics segment [laptops and smartphones], and demand for memory has declined. But demand for higher performing data center chips, defense, and automotive chips remains historically high, with some semiconductor companies seeing growth in the area of 40% or more."

One potential nearer-term solution for companies that rely on semiconductors: advanced data analytics and artificial intelligence tools to help manage supply issues.

"The Covid-19 pandemic vividly illustrated the impact that unexpected events can have on global supply chains," said Rohit Tandon, managing director and global AI & analytics services leader at Deloitte. "However, AI can help the world avoid similar disruptions in the future."

By crunching through the massive amounts of data being generated by today's supply chains, AI can predict a range of unexpected events, such as weather conductions, transportation bottlenecks, and labor strikes, helping to anticipate problems and reroute shipments around them, Tandon said.

"AI can also enable dramatic improvements in other key supply chain areas, including demand forecasting, risk planning, supplier management, customer management, logistics, and warehousing," Tandon said.

This can lead to improved operating efficiency and working capital management, greater transparency and accountability, and more accurate delivery estimates; and fewer supply disruptions, Tandon said. "In addition, manufacturers that are using AI for visibility in their smart factory operations can better respond to potential disruptions to avoid delays and pivot if needed, enabling them to be more resilient while continuing to meet customer demands," he said.

"Organizations can leverage data analytics tools for deeper insights across the supply chain," Tandon said. "These tools are designed to improve demand prediction and support data sharing with customers and partners." In addition, organizations can use AI to predict or forecast supply chain-related events such as logistics challenges, geopolitical issues, and supply disruptions.

They can either execute actions autonomously or recommend actions stakeholders should take, "ultimately helping companies build resilience into their supply chains," Tandon said.

When deploying these tools for supply chain management, it's a good idea to start with a small and narrow scope and evolve the depth and breadth of the models and algorithms as the results show their accuracy and value, Tandon said.

High-quality data is also important. "Underlying data is key, as bad data equals bad analytics," Tandon said."Lack of transparency across the supply chain is often the result of inconsistent and incomplete data across product, supplier and customer.Standing up data governance processes that align to common definitions and [fixing] data issues provides the foundation of data quality that builds trust in the output of the analytics and AI process."

Rand Technology, an independent semiconductor distributor, is using data analytics to solve customer challenges related to supply.

"For example, if a customer has a need to alleviate inventory surplus, we use data and analytics to identify other users of these products and create an opportunity to rehome them," said Jennifer Strawn, vice president of solutions and sourcing for the Americas and EMEA at Rand. "In this way, OEMs and contract manufacturers are able to shore up their inventory mix of components."

In addition, data and analytics are especially important during a manufacturer's new product introduction phase in the bill of materials selection, Strawn said. "It is critical, during this phase, to identify where you can build flexibility into the design so that there are multiple sources for semiconductors on the approved list of materials," she said.

In this way, manufacturers are not reliant on a single semiconductor provider, which in the current environment could impact business. "We leverage advanced analytics to help determine the availability of these semiconductors and to spot trends and patterns, such as gaps, price increases or product change notices, before products are in production," Strawn said. Rand also uses the technology to drive decisions on future scenarios and to determine how much buffer stock a company might want to secure, she said.

Rand also uses advanced data analytics to identify trends and patterns that enable it to guide customers strategically through perilous market conditions. "With modeling and real-time visibility into availability, market shifts and conditions globally," Strawn said, "we are able to help reduce risks and map strategies in advance that can be employed when we note certain changes and disruptions in the industry."

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How using analytics and AI can help companies manage the semiconductor supply chain - CNBC

Hospitals and health systems are largely using artificial intelligence for purposes like patient scheduling and disease prediction, several digital and data executives told Becker's.

That lines up with findings of an Oct. 18 Deloitte report on the state of AI, which found the top healthcare applications include customer service operations and computer-assisted diagnostics.

At Winston-Salem, N.C.-based Novant Health, for example, is using AI to optimize scheduling and cut down on patient wait times. The health system also employs AI for its hospital-at-home program and other remote monitoring efforts.

"By combining our diagnostics, predictive health scoring, as well as vitals management, we are looking for being able to move as much to avenues that will provide the patient with a more comfortable and familiar environment," said Karl Hightower, chief data officer and senior vice president of data products and services of Novant Health.

According to the Deloitte report, the top AI uses for patient experience and marketing were customer service operations and image recognition/digital radiology (tie, 41 percent); computer-assisted diagnostics (40 percent); personalization and patient vitals monitoring (tie, 38 percent); and omnichannel experience management (35 percent). The consulting firm surveyed 260 global healthcare and life science organizations from across the globe.

"The leading industry-specific healthcare AI use cases focus on outcomes and monitoring in such potentially transformational areas as AI-assisted diagnoses including predictive diagnoses, patient engagement, insurance fraud detection and smarter hospitals," the report stated.

The data and digital health leaders interviewed by Becker's found these categories to be largely representative of their uses of AI.

"Mayo Clinic has AI projects that fall under the diagnostics, customer service, and patient vitals monitoring categories, but we also have many AI initiatives that would be considered operational, or administrative, uses," said Ajai Sehgal, chief data and analytics officer of the Rochester, Minn.-based health system.

Those include patient volume prediction, resource allocation, and billing. At Mayo's Center for Digital Health, most use cases involve natural language processing, taking spoken word or text and turning it into a structured format.

Mayo Clinic's digital front door app has a natural language processor to match a patient's primary concern to the health system's scheduling tree to expedite new appointments.

New York City-based NewYork-Presbyterian Hospital has deployed such tools as predictive machine learning, robotic process automation and AI imaging.

"Particularly, over the course of the last several years, and because of the COVID-19 pandemic, we've heavily leveraged AI within our customer service operations," said Peter Fleischut, MD, senior vice president and chief transformation officer of NewYork-Presbyterian.

Its NYP Connect App offers self-scheduling, EHR access and telehealth visits. Patient-facing calls are automated through scheduling and conversational AI.

Most of the AI efforts at Duarte, Calif.-based City of Hope fall under clinical decision support, a combination of computer-assisted diagnostics, vital sign monitoring and risk detection, said Chief Digital Officer Mark Hulse.

City of Hopes combines AI, genomics and other clinical data to predict the risk of sepsis for transplant patients, 30-day unplanned readmissions, surgical complications, and survival and early disease. AI also helps forecast financial metrics and patient readiness for early discharge.

At Sioux Falls, S.D.-based Sanford Health, the enterprise data analytics team has developed algorithms to try to predict which patients might be at risk of Type 2 diabetes or which women should have mammograms earlier than recommended.

"We are applying predictive models and risk stratification to our electronic medical record that consider and review all aspects of patients' histories to inform clinical decision-making," said Doug Nowak, vice president of enterprise data analytics for Sanford Health.

Jacksonville, Fla.-based Baptist Health works with AI to cut back on repetitive tasks for employees and optimize operating room scheduling.

"My prediction is that as data is shared more widely, coupled with significant increases in computational ability, and we turn towards utilization of quantum computing for big data analysis, AI will truly be unlocked with numerous more categories and hard return-on-investment use cases," said Aaron Miri, Baptist Health's senior vice president and chief digital and information officer.

The rest is here:

Where health systems are spending their AI dollars - Becker's Hospital Review

New AI image-generation systems make headlines every day but that revolution started many years ago. Now one of the most established services for AI face generation has expanded its offering to include full body images. An early use of computer-made faces was for news stories, video games, and documentaries when a person was needed to convey an idea or represent an unknown individual for which no photo was available. Keeping a stock library of faces isnt too difficult for an agency but standing poses are harder since the type of clothing affects the possible uses of the images. In the past, one or more models would need to be hired for these types of shots.

Generated Media, the company behind the Generated Photos website and its free, real-time Face Generatorrecently launched a massive library containing 100,000 AI human images that look like real people, wearing a variety of clothes and standing in relaxed-looking poses with neutral facial expressions.

There is some concern about how the rapid advancement of artificial intelligence will affect various industries and it seems artists, actors, and models arent even safe from computer technology. When you see a person in an advertisement or shopping website, are they even real? It might be hard to tell. Whats clear is this revolution will continue to accelerate until everyone will either be working with computers or replaced by machines.

Generated Photos AI-created humans are still in development and the full set must be downloaded in a 1.3GB zip file in order to view individual images. Despite such a large collection, the library is limited to young, slim adults at the moment. That will be expanded, according to a representative from the company, to include different ages, body types, various poses, and custom clothes, as has been done with faces.

Eventually, a searchable database that works like the Face Generator will be available. The Generated Photos website also includes an anonymizer feature that allows you to upload a selfie and the system will find the closest match from its database of millions of faces, so you can post a photo online thats similar to your real appearance but not actually you. This and the other tools are free for personal use and can be licensed for commercial use.

The industrial revolution saw the loss of labor-intensive jobs and the AI revolution will soon be coming for creative and analytical workers. In a utopian future, all work would be the domain of machines whether taking the form of robots or operating virtually over the internet, leaving humans to lives of leisure. The tricky part will be navigating the transitionary time.

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Watch out models, the AI revolution is coming for your jobs too - Digital Trends

It's absolutely mind-blowing how much artificial intelligence has come to play a central role in photography. There's the presence of it in everyday usage in smartphones, with things like portrait mode and night sight, but now, even more powerful tools are available to desktop retouchers. Just check out how it works on this 70-year-old photo.

Coming at you from Glyn Dewis is a video that incorporates a bunch of different tools in the service of restoring a damaged 70-year-old family photo of his. While ordinarily, this would be a quick retouch job to remove scratches and tears, in this particular case, Dewis wanted to be able to not only do that, but increase the resolution and make it an aspect ratio that would work for an 8x10" print as well.

The first tool Dewis uses should be pretty familiar to most photographers, and that's one of Adobe Photoshop's neural filters, the photo restoration filter, which does a decent job of most basic cleanup functions. As he points out, there are some other functions there to colorize photos, but for this video, he sticks to black and white.

The mind-blowing part came in how Dewis changed the aspect ratio of the photo. Using a tool called DALL-E 2, which describes itself as "a new AI system that can create realistic images and art from a description in natural language." Dewis was able to pop his photo into the system and have the AI fill in the missing background details quite convincingly, such that he now had enough headroom to crop into the aspect ratio he wanted.

I got so wrapped up in how this tool works. It's not perfect by any stretch of the imagination, but Dewis' use case is an interesting one for photographers.

The last step is a piece of software that's not as esoteric as DALL-E, and that's Topaz Gigapixel AI, an image upscaler that can increase the size of an image by 600%, since DALL-E is a bit limited in its output resolution.

With those three tools, Dewis was able to retouch his old photo with much less effort than it would have taken in the past.

What do you think of the results?

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Using AI to Restore Old Photos Actually Works - Fstoppers

Symbl.ai Enhance Conversation Intelligence Accessibility With Managed Libraries and Recommendations

SEATTLE (PRWEB) October 19, 2022

Symbl.ai, a developer-first platform providing best-in-class conversation intelligence (CI), has announced two new breakthrough features designed to make the technology more accessible for developers with little to no expertise in conversation AI or machine learning.

Symbls CI platform allows users to track a wide array of actionable insights from conversations, both in real-time and asynchronously. These insights include topics of discussion, questions raised, action points, as well as tone, sentiment and intent, all of which can be used to provide automatic summaries of conversations, score leads, train staff and more.

Users can define their own groups of keywords and phrases with similar meanings known as trackers, which the technology then uses to provide actionable insights. Previously, the onus was on businesses themselves to create and grow their library of trackers, resulting in a cold start problem for those with limited resources or experience. To grow its tracker library, a company would need to invest a great deal of time watching, listening or reading through conversations to grow their tracker library manually, resulting in missed tracker opportunities and slow time to market.

Symbl.ai has now rolled out a verticalized managed libraries feature which eliminates the cold start problem and allows businesses to start using CI functionality out of the box with a predetermined and constantly updated library of trackers. Companies can select a library that best suits their industry and get started immediately, adding their own tracker groups in just a few clicks.

A new personalized recommendations feature has also been introduced which uses Symbl.ai technology to identify new tracker opportunities as they emerge and notify users. Recommended trackers can be added seamlessly, allowing businesses to grow their tracker libraries intelligently from the outset without the need for manual input.

When we first launched Trackers, customers were excited to use the functionality because it provided them with the flexibility to track insights appropriate for their specific use cases, commented Surbhi Rathore CEO, Symbl.ai. However, we quickly realized that some customers were struggling to get started with tracker functionality, primarily because they had to develop their own library of trackers to get the most out of the feature. We listened to their feedback and are now launching the managed trackers library and recommendations features to give customers a running start and allow them to generate actionable insights from the outset.

To take advantage of trackers and recommendations, users simply need to login to the Symbl.ai platform and select the trackers most appropriate to their industry or use-case. Next, users can process conversations from the platform and view the results of managed trackers to see how they are performing. Managed trackers can be customized to include more phrases specific to a given business or product. As conversations are processed, live recommendations will appear based on conversations that have been recorded in the past 30 days. If a user accepts a recommendation, it will be seamlessly integrated into their existing tracker library.

To learn more about managed trackers and proactive tracker recommendations, users can access the documentation and FAQs here.

About Symbl.aiSymbl.ai is a Conversation Intelligence (CI) platform to rapidly deploy real-time conversation understanding at scale on any communication channel. Our comprehensive suite of APIs unlock proprietary machine learning to generate summarization, track intents, actionable insights, sentiments, and knowledge contextually across audio, video, or text conversation data. For more information, visit https://symbl.ai/. Follow us on social media: Follow us on social media:https://www.linkedin.com/company/symblai/Linkedin,Twitter,Instagram,https://www.facebook.com/symbldotai/Facebook,https://www.youtube.com/channel/UCpqOyNdFI0kASCZL-eCnkcAYouTube,https://github.com/symblaiGithub

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Symbl.ai Enhance Conversation Intelligence Accessibility With Managed Libraries and Recommendations - PR Web

ARLINGTON, Va. U.S. military researchers will brief industry next month on an upcoming project to develop new kinds of artificial intelligence (AI) and machine autonomy for battle management and sensor fusion.

Officials of the U.S. Defense Advanced Research Projects Agency (DARPA) in Arlington, Va., issued a special notice (DARPA-SN-23-06) on Monday for the Artificial Intelligence Reinforcements (AIR) project.

The DARPA AIR initiative seeks to fill gaps in research on developing and deploying tactical autonomy capability in real-world military operations. Industry briefings will be from 8:30 a.m. to 5 p.m. on Monday 14 Nov. 2022 at Amentums Ballston Conference Center, 4121 Wilson Blvd., in Arlington, Va.

AIR will focus on previously avoided dimensions to enable tactical autonomy in integrated sensors, scalability to large engagements, adaptability to changing conditions, and the ability to learn predictive models that incorporate uncertain knowledge of adversary and self, as well as deceptive effects.

Related: Marines ask Sentient Vision for artificial intelligence (AI) and machine autonomy for unmanned reconnaissance

AIR will pair existing, maturing, and emerging algorithmic approaches with expert human feedback to evolve the cooperative autonomous behaviors rapidly that solve previously avoided challenges.

AIR will address two technical areas: creating fast and accurate models that capture uncertainty and automatically improve with more data; and developing AI-driven algorithmic approaches to real-time distributed autonomous tactical execution within uncertain, dynamic, and complex operational environments.

The AIR program also will develop ways to design, test, and implement future iterations of AIR software.

Related: Artificial intelligence (AI) to enable manned and unmanned vehicles adapt to unforeseen events like damage

Briefings will be at Amentums Ballston Conference Center on the second floor of 4121 Wilson Blvd. in Arlington, Va., on Monday 14 Nov. 2022 from 8:30 a.m. to 5:00 p.m. Check-in begins at 8 a.m.

Briefings will include information that is International Traffic in Arms (ITAR)-restricted, so attendance is limited to U.S. citizens or U.S. permanent residents representing U.S. companies. Briefings will be classified at the Collateral SECRET level and will require security clearances.

Those interested in attending should register online at https://creative.gryphontechnologies.com/darpa/tto/air/pd/. Those seeking to attend should fax their security clearances and visit requests to Amentum at (571) 428-4358. Registration closes on 4 Nov. 2022.

Related: Researchers ask industry for enabling technologies in artificial intelligence (AI) and machine automation

Those attending may meet individually with the Air program manager, Lt. Col. Ryan "Hal" Hefron, on Tuesday 15 Nov. 2022. Email DARPA-SN-2306@ darpa.mil to request an individual session.

One-on-one meetings will be at DARPA at 675 North Randolph St. in Arlington, Va., and will require security clearance. Fax clearance/visit requests to DARPA at (703) 528-3655 or send via encrypted e-mail to VWC@darpa.mil.

Email questions or concerns to Lt. Col. Ryan Hefron at DARPA-SN-23-06@darpa.mil. More information is online at https://sam.gov/opp/1b972abff6de4a2fbf7999af316e52c0/view.

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Military researchers to brief industry on artificial intelligence (AI), sensors, and autonomy program - Military & Aerospace Electronics

DENVER--(BUSINESS WIRE)--AMP Robotics Corp. (AMP), a pioneer in artificial intelligence (AI), robotics, and infrastructure for the waste and recycling industry, is developing an AI-powered automation solution to improve recovery of film and flexible packaging. This first-of-its-kind innovation for materials recovery facilities (MRFs) aims to tackle the persistent challenge of film contamination.

A mere 1 percent of U.S. households have curbside access for recycling film and flexible packaging today, estimates The Recycling Partnership. Yet film and flexibles comprise the fast-growing and second-largest valued packaging segment, behind only corrugated containers and ahead of bottles and other rigid plastic packaging. Close to 95 pounds of these materials, including grocery and storage bags, pouches, and wrappers, are found in the average U.S. home each year.

The recycling industry lacks infrastructure for the identification and separation of film and flexible packaging, and these materials jam MRF equipment not designed to handle it. Even 2 to 3 percent film in overall MRF streams can be unmanageable to remove manually, often damaging equipment, necessitating downtime, and hindering recovery of recyclables. Film and flexible packaging find their way into every line in a MRF, resulting in high levels of contamination. But most of these materials, given their light weights, make their way onto fiber lines. Film contamination degrades fiber bale purity, leading to revenue loss or the need for additional post-processing downstream.

AMPs solution, AMP Vortex, is the industrys first AI-powered automation system for film removal and recovery in MRF environments. AMPs system targets film contamination and is initially optimized for quality control on fiber lines. Vortex provides the industry with the most flexible and adaptable solution targeting film; it can be deployed as a retrofit solution in various configurations to accommodate different belt sizes and inclines.

Innovation and infrastructure improvements are vital to helping MRFs process this challenging, prolific material type and increase recycling rates for residential film and flexible packaging, said Matanya Horowitz, founder and CEO of AMP Robotics. AI is laying the groundwork to reduce the contamination burden on MRFs and scale the recycling of film and flexible packaging.

Because these materials are complicated and expensive to reprocess into raw materials, end markets for film and flexible packaging have been limited. While flexible packaging has been almost uniformly single-use, major brands continue to make commitments to use more recycled content in their products, and several states have recently adopted laws aimed at ramping up the use of post-consumer resin in plastic products and packaging. AMP is developing Vortex to target and recover film and flexible packaging for baling and selling.

Amanda Marrs, senior director of product for AMP Robotics, added, With our latest technology innovation for more efficient, profitable recycling operations, we aim to boost recovery and drive demand for products manufactured from recycled film and flexibles to develop and support end markets. This effort is key to addressing the plastic waste crisis and diverting millions of tons of recoverable material from landfills annually.

Vortex emerged from AMPs Customer Innovation Program (CIP), a technology program focused on collaborating with industry stakeholders to develop new AI-enabled automation applications for the recycling industry. Vortex is among a portfolio of new products and performance features AMP is developing for pilot and commercial release in support of increased recycling efficiency and improved cost-effectiveness for MRFs and converters.

In alignment with its efforts to economically recover this material type, AMP is a member of The Recycling Partnerships Film and Flexibles Recycling Coalition, part of The Partnerships Pathway to Circularity Initiative, a broad group of industry stakeholders seeking to increase curbside collection of film recycling and support end markets for film and flexible products. The Coalitions primary focus in 2022-2023 is proving efficient and effective collection through pilot projects, as well as infrastructure and optimization grants.

AMP has started its pre-release of Vortex to the market, actively working with initial customers on deployment. The company expects to be in full production release in 2023.

About AMP Robotics Corp.

AMP Robotics is modernizing the worlds recycling infrastructure by applying AI and automation to increase recycling rates and economically recover recyclables reclaimed as raw materials for the global supply chain. The AMP Cortex high-speed robotics system automates the identification and sorting of recyclables from mixed material streams. The AMP Neuron AI platform continuously trains itself by recognizing different colors, textures, shapes, sizes, patterns, and even brand labels to identify materials and their recyclability. Neuron then guides robots to pick and place the material to be recycled. Designed to run 24/7, all of this happens at superhuman speed with extremely high accuracy. AMP Clarity provides data and material characterization on what recyclables are captured and missed, helping recycling businesses and producers maximize recovery. With deployments across North America, Asia, and Europe, AMPs technology recovers recyclables from municipal collection, precious commodities from electronic scrap, high-value materials from construction and demolition debris, and valuable feedstocks from organic material.

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AMP Robotics Develops Industry's First AI-Powered System for Recovery of Film and Flexible Packaging - Business Wire

There's a lot of talk about how artificial intelligence (AI) is changing the world. And it's true -- AI has already started transforming the healthcare, finance, and manufacturing industries. But there's one area where AI is causing some severe problems: fake reviews.

We spoke with Truely CEO JP Bisson about AI technology and how companies can use it to protect their interests.

BN: How has AI technology impacted the way we live and work?

JB: Artificial intelligence had come a long way since the 50s, when Alan Turing asked, 'Can machines think?' Although no machine has even come close to passing the Turing Test, that question sparked the decades-long research that has brought us AI technology as we know it today.

We are probably unaware of it, but AI is now a considerable part of our everyday lives. We can play a song, turn house lights on and get answers to questions using voice assistants. We get personalized content on our social media feeds and video streaming services. We unlock our phones and open our apps with facial recognition. We get to our destination in self-driven vehicles.

At work, we have software programs that automate operational processes, allowing companies to save resources. We have tools that analyze data, software that measures employee experience, robots that speed up product manufacturing, apps that write content. Recently I have heard of two AI systems in the works -- one that produces realistic visuals and artwork from a description in plain language and another that automates manual tasks in-browser.

It is all so amazing that I can't wait to see what happens next. Still, we must look at AI as a double-edged sword that could cause grave harm if it falls into the wrong hands.

BN: In what ways can AI cause this harm?

JB: We live at an exciting time when almost everything we set our mind to becomes possible, especially with AI technology. But just like I said, people can also use artificial intelligence to harm and destroy the things the technology has helped build.

Case in point: deep learning AI is now used to create deceptive images and videos or deepfakes. AI writing tools have been taught to generate false news and fake reviews. What's scary about all these fakes is that they seem real. To the unsuspecting reader or viewer, they are believably true.

The internet has become a great source of information, and rightfully so. You only need to type a word on any browser, and you'll get pages of results. So you can't blame why people who aren't so discerning believe almost everything they read and see on the web. Unfortunately, the internet has developed into a medium where people no longer necessarily tell the truth.

These untruths have led us to develop a site that would help bring truth back to the internet. We understand how fake reviews hurt not just the product and service providers but also the consumers. Truely's aim is to stop these fake reviews -- which can be mass produced by AI -- and bring the truth back to the internet. And we are fighting fire with fire, using AI technology to identify and remove these fake reviews -- whether written by paid humans or AI-generated -- to give consumers the real score about the products and services we feature on our site.

BN: Can you tell us more about the AI technologies used to create fake reviews, how these technologies work, and how artificial intelligence can filter out fakes?

JB: Generally, artificial intelligence has four components: machine learning (ML), natural language processing (NLP), computer vision, and robotics. Out of those four, people use ML and NLP to generate text.

Machine learning allows computer computers to learn from data without being explicitly programmed. This AI system is based on the idea that machines should be able to learn and improve independently, just like humans.

There are two main types of machine learning:

Under machine learning, we have deep learning, which is a powerful tool for generating text. So you feed a computer a large body of text, and from there, the system learns the distribution of words and grammar rules.

Once the system has learned the rules and what-have-you, it can generate new realistic and coherent text. And yes, this technology has been very helpful in quickly creating written content such as news articles, product descriptions, text summaries, and more. Unfortunately, people also use it to create fake news, reviews, and comments -- which, as we know, can be detrimental not just for businesses who get these fake ratings but for society as a whole.

Natural language processing, on the other hand, is a form of artificial intelligence that allows computers to understand and process human language. This system works by converting human language into a format that computers can understand. AI algorithms are then used to analyze and interpret human language. With these algorithms, a computer can understand the overall meaning of a text, identify named entities, extract key phrases, and even find the sentiment of a text. NLP is becoming more and more commonplace in our everyday lives, with people using it for the same use cases they use ML for.

At Truely, we use these technologies to filter out fake reviews. Our AI analyzes reviews for certain red flags and language patterns, indicating that the reviews are not genuine. It also looks for patterns in the language used. For example, fake reviews often use superlative words to describe a product, while real reviews are more likely to use neutral language. It also uses other clues to identify and filter out fake reviews on the list of software products and services on the Truely site.

The tools we use have proven to be valuable in the fight against fake reviews. As AI technology evolves, I am optimistic that it will become more reliable in weeding out fake reviews.

BN: What does this technology mean for SaaS providers and businesses?

JB: There is only one thing I'm sure of: the world of online reviews is changing. This AI technology is already significantly impacting businesses, especially those that rely heavily on online reviews.

Generally, this AI technology is good news for businesses, as it means they no longer have to worry about the negative impact of fake reviews.

Companies that rely on fake positive reviews to boost their ratings will now be more likely to be exposed, leading to a loss of customer trust and potential legal problems.

Overall, AI that removes fake reviews is a significant development for businesses. Besides exposing fake reviews, it protects the credibility of SaaS firms and other companies.

Photo Credit: Bacho/Shutterstock

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Harnessing the power of AI to combat fake reviews [Q&A] - BetaNews

Organizations now have a rapid, precise, and comprehensive solution for their data matching needs

BOSTON, Oct. 19, 2022 /PRNewswire/ --Syniti,a global leader in enterprise data management, today announced the upcoming availability of its fully cloud-native Syniti Match, the most rapid and precise AI-powered data matching software on the market that supports both party and operational data. Enterprise data is often riddled with errors and consistencies like typos, missing fields, duplications, and name variances. Those errors are compounded when dealing with multiple systems which is increasingly common as organizations continue to modernize legacy operations. Syniti Match is purpose-built to handle data complexity, no matter the shape, source, or type of enterprise data, including customer, ERP, supply chain and business data.

Syniti has replicated the natural intelligence humans use when forming comparisons, and with Syniti Match, can deliver that capability at scale. AI, proprietary phonetic and fuzzy matching algorithms, and context-sensitive lexicons evaluate matches contextually, allowing the software to understand data based on what it is, rather than where it resides in a table. It maximizes matches found while minimizing false positives and can easily scale as business data grows in volume and variety. With a flexible SaaS-based model, Syniti Match can be deployed from anywhere, anytime without installation.

Emily Williams, vice president of product alliances, Syniti, said:""The ability to match both types of data quickly and accurately fills a very necessary gap in the market as it benefits every part of an organization from operations to sales and marketing. With Syniti Match, we are reducing the wasted time spent on the tedious processes of eliminating the duplications and inconsistencies that drive down the quality of your data."

Kevin Campbell, chief executive officer, Syniti, said: "Organizations today simply can't afford to base any decisions on poor quality data. At Syniti, we keep finding new and innovative ways to enable our customers to achieve the accurate and trustworthy data they need to run and grow their businesses."

Jon Severn, circulation director, MJH Lifesciences, said, "MJH Lifesciences is the largest privately held medical media company in the U.S. We have a constant flow of new data sources into our system which makes keeping the data free of duplicates very difficult. Previously, we were de-duping files manually, and spending more time than we would like doing so - and delivering less accurate results. With Syniti Match, our current process is much more accurate, resulting in greater audit compliance and we have reduced incidents of unintentional duplicates."

Register for One Duplicate Is All It Takes: How to Prevent the Bad Data Domino Effect to learn about establishing a proactive data quality strategy and what you should be looking for in a data matching solution.

About Syniti Synitisolves the world's most complex data challenges by uniquely combining intelligent, AI-driven software and vast data expertise to yield certain and superior business outcomes. For over 25 years, Syniti has partnered with the Fortune 2000 to unlock valuable insights that ignite growth, reduce risk and increase their competitive advantage. Syniti's silo-free enterprise data management platform supports data migration, data quality, data replication, master data management, analytics, data governance, and data strategy in a single, unified solution. Syniti is a portfolio company of private equity firm Bridge Growth Partners LLC. Read more atwww.Syniti.com.

SOURCE Syniti

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Syniti Introduces Syniti Match, the Industry's First AI-Driven Matching Solution to Support Both Party and Operational Data - PR Newswire