AI4Bharat recently introduced Chitralekha is an open-source AI-powered video transcreation platform, developed by AI4Bharat and EkStep. It has an integrated workforce management system, which enables end-to-end transcreation of a video from one language to another through the stages of transcription, translation and voice-over for the translated language.
It presently caters to English and various Indian languages, supporting 12 languages for transcription, all 22 official languages for translation, and 14 languages for automated voice-over. Additionally, it allows transcription editing of mp3 audio files. The process of transcreating educational and informative videos from one language into multiple others contributes to wider accessibility and audience reach.
Chitralekha employs the open-source subtitle editor tool, Subplayer, accessible at https://subplayer.js.org/, to construct its subtitling system. Through the utilisation of open-source models such as IndicASR, IndicTrans, and IndicTTS developed by AI4Bharat, Chitralekha has the capability to automatically generate transcription, translation subtitles, and voice-over for the translated text in any given video.
It’s worth noting that, at present, machine-generated voice-over is specifically designed for single-speaker videos, with ongoing efforts to extend support for multi-speaker videos.
Chitralekha orchestrates a meticulous workflow encompassing transcription, translation, and voice-over stages. Rigorous verification and correction processes for transcripts and machine-generated subtitles set the stage for refined transcreation. Users are empowered to contribute manually, ensuring flexibility and accuracy throughout each stage.
Chitralekha empowers users to download subtitles in various formats, such as srt, vtt, txt, and docx. The platform goes a step further, offering word-level aligned transcript subtitles in ytt format, dynamically highlighting the spoken word. Project management is streamlined through effective grouping, customizable workflows, and robust workforce management.
Moreover, its user-friendly interface provides customisation options, including font size adjustment, video playback speed, theme selection, and subtitle positioning. This emphasis on user-centric features enhances the overall usability of the platform.
The post AI4Bharat Introduces Chitralekha, AI Video Transcreation Platform appeared first on Analytics India Magazine.
The Indian Space Research Organization (ISRO) has joined forces with SpaceX to launch the GSAT-20 communications satellite, weighing 4.7 tonnes, using SpaceX’s Falcon-9 rocket. The announcement was made by NewSpace India Limited (NSIL) on Wednesday.
The GSAT-20 satellite is strategically designed to cater to India’s escalating needs in broadband, in-flight and maritime communications (IFMC), and cellular backhaul services, according to NSIL. Renamed GSAT-N2, this satellite will feature Ka-Ka band high throughput satellite (HTS) capacity, equipped with 32 beams for pan-India coverage, extending to the Andaman and Nicobar Islands and Lakshadweep.
NSIL disclosed that a significant portion of the HTS capacity on the GSAT-20 satellite has already been secured by Indian service providers, refraining from disclosing specific details about the buyers.
Read: SpaceX is All About Collaboration
Due to the payload requirements, ISRO opted to utilise SpaceX’s Falcon-9, as its GSLV-Mk3 heavy satellite launch rocket lacks the capability to accommodate the 4,700 kg GSAT-20 in Geosynchronous Transfer Orbit (GTO). NSIL emphasised that the launch is executed under a contract between NSIL and SpaceX.
SpaceX’s Falcon-9, renowned for its robust capabilities, can deploy payloads of up to 8,300 kg into GTO. This marks a departure from ISRO’s historical reliance on French company Arianespace for launching heavier satellites.
Highlighting the satellite’s specifications, NSIL pointed out, “GSAT-20, weighing 4,700 kg, offers HTS capacity of nearly 48 gpbs. The satellite has been specifically designed to meet the demanding service needs of remote and unconnected regions.”
Read: ISRO’s Missions to Watchout for in 2024
As part of space sector reforms initiated by the government in June 2020, NSIL has been tasked with the responsibility to construct, launch, own, and operate satellites to meet user service demands. In June 2022, NSIL successfully executed its first demand-driven satellite mission, GSAT-24, which was fully funded by NSIL, and the capacity onboard was secured by M/s TataPlay.
Currently, NSIL manages and operates 11 communication satellites in orbit, underscoring India’s commitment to advancing its capabilities in space exploration and satellite technology.
The post ISRO, SpaceX Partner for GSAT-20 Satellite Launch appeared first on Analytics India Magazine.
Intel spins out a new enterprise-focused GenAI software company Kyle Wiggers 9 hours
Intel, intent on making bigger moves in the market for AI-powered enterprise software, is spinning out a new platform company with the backing of Boca Raton, Florida–based asset manager and investor DigitalBridge.
Called Articul8 AI (an awkward abbreviation of “Articulate AI”), the new entity builds off a proof-of-concept from an Intel collaboration with Boston Consulting Group (BCG) early last May. Reuters reports that Intel, using its hardware and a combination of open source and internally sourced software, created a generative AI system that can read text and images — running inside BCG’s data centers to address BCG’s security requirements.
The system was developed within Intel over the course of two or so years. But it was more recently fine-tuned for BCG’s specific uses, according to CRN.
Initially, BCG was the sole go-to-market supplier and customer of the system. Over the last few months, however, Intel’s worked to scale the platform — which is optimized for Intel hardware but supports alternatives — to companies in financial services, aerospace, semiconductor, telecommunications and other industries that “require high levels of security and specialized domain knowledge,” according to an Intel spokesperson.
“Articul8’s gen AI software product was built from the ground up to address the needs of enterprises and is optimized for speed of deployment, scalability, security and sustainability — including costs,” the spokesperson told TechCrunch via email. “The Articul8 platform delivers AI capabilities that keep customer data, training and inference within the enterprise security perimeter. The platform also provides customers the choice of cloud, on prem or hybrid deployment.”
Arun Subramaniyan, formerly a VP and GM at Intel’s data center and AI group, will become the spinout’s CEO. The rest of the Articul8 team will also comprise ex-Intel employees, and Intel will retain an undisclosed stake in the firm.
Beyond Intel and DigitalBridge, which is publicly traded and a major investor in data centers, Articul8 investors include Fin Capital, Mindset Ventures, Communitas Capital, GiantLeap Capital, GS Futures and Zain Group.
“Intel and Articul8 will remain strategically aligned and Intel plans to leverage Articul8’s enterprise gen AI software for internal use cases as well as offer it to end customers as part of a joint go-to-market partnership,” the spokesperson said. “This collaboration will drive consumption of Intel compute offerings [and] Intel will continue to leverage Articul8’s AI domain knowledge and expertise as Intel continues to grow its footprint in the generative AI market.”
Reuters notes that Intel’s move to launch Articul8 is its latest endeavor to seek outside capital for business units. The chipmaker spun out car chip firm Mobileye, sold off its memory chip division and intends an eventual initial public offering of its programmable chip unit.
The spinouts are part of Intel’s strategy to raise capital for CEO Pat Gelsinger’s comeback plan, which involves building out new chip factories in the U.S. and Europe, as well as introducing new advanced chip manufacturing nodes within the next four years. In particular, Articul8 fits into Gelsinger’s plans to deliver new software products and services — including GenAI-powered products — that rival those from competitors like Nvidia and AMD and make Intel hardware more attractive for a range of applications.
MIT Researchers Leverage AI To Identify Antibiotic That Can Kill Drug-Resistant Bacteria January 3, 2024 by Ali Azhar
(cones/Shutterstock)
As bacteria continue to evolve to withstand the effects of antibiotics, it has rendered bacterial infections more challenging to treat. The issue of “antibiotic resistance” has already become a critical health issue.
The overuse and misuse of antibiotics have made the issue worse. Researchers have now turned to artificial intelligence to find innovative ways to fight against this issue and MIT researchers might have found a way to crack the code.
Using artificial intelligence, MIT researchers identified a new class of antibiotics that could kill a drug-resistant bacterium that causes more than 10,000 deaths in the U.S. every year. The research was based on using deep learning – a type of AI that teaches computers to process data in a way that is inspired by the human brain.
The researchers were successful in showing that these compounds could kill methicillin-resistant Staphylococcus aureus (MRSA), which is resistant to several types of antibiotics including methicillin, penicillin, and amoxicillin. MRSA can cause various infections including some that are life-threatening.
The compounds identified by the MIT research that can kill drug-resistant bacteria also show very low toxicity when tested with human cells, making them good candidates for human use.
(Image courtesy: IBM)
James Collins, one of the lead researchers of the study and the Termeer Professor of Medical Engineering and Science at MIT says “The insight here was that we could see what was being learned by the models to make their predictions that certain molecules would make for good antibiotics. Our work provides a framework that is time-efficient, resource-efficient, and mechanistically insightful, from a chemical-structure standpoint, in ways that we haven’t had to date.”
AI has recently been at the forefront of research in the world of medicine and healthcare. Last month, researchers from the Oxford Martin School published a ground-breaking study where they used AI to detect antimicrobial resistance (AMR). This new advancement is set to pave the way for novel and rapid antimicrobial susceptibility tests.
Using deep learning for new drugs is not a new phenomenon but as AI models become more sophisticated, the capabilities of such systems get stronger. One of the key insights of this study was the researchers were able to pinpoint what kind of data is used by deep learning models to make antibiotic potency predictions.
This knowledge can empower other researchers to develop drugs that might work even better than the ones identified by this study. According to Felix Wong, the lead co-author of the MIT study, the study will help “open the black box” to help other researchers understand how DL models work.
The MIT researchers have shared their findings with Phare Bio, which is a social venture using novel AI and Deep Learning to tackle the world’s most urgent threats. Collins is one of the founders of Phare Bio. The startup plans on doing a more detailed analysis of the data and identifying potential clinical use cases of the compounds. Meanwhile, the authors of the study will focus on using their DL models to seek compounds that can kill other types of bacteria.
This article originally appeared in Datanami.
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About the author: Alex Woodie
Alex Woodie has written about IT as a technology journalist for more than a decade. He brings extensive experience from the IBM midrange marketplace, including topics such as servers, ERP applications, programming, databases, security, high availability, storage, business intelligence, cloud, and mobile enablement. He resides in the San Diego area.
Every year, prospective college students are challenged with writing an admissions essay that not only tells their story but also makes them stand out in a way that compels the admissions officer to accept them. A new AI tool could transform that process.
Researchers at the University of Colorado Boulder have developed AI tools that can read and evaluate college admission essays, according to a report from Daily Camera.
Also: Generative AI can be the academic assistant an underserved student needs
Partnering with the Common App, the researchers assembled a data set of 300,000 students who applied to college in 2008 and 2009, gathering a random sample of essays that were read by humans and rated on personal qualities such as leadership, mindset, and teamwork.
The AI tool was modeled to do the same, and the research team found that the AI-generated ratings matched closely with the human ratings. According to the report, the AI's ratings could also predict fairly accurately whether or not a student would graduate.
The tool is meant to help students who might otherwise get overlooked by highlighting qualities within the essay that may make them great candidates for admission, even if the essay itself wasn't perfectly written.
These tools have another advantage: They can circumvent the personal bias that an admissions office could hold regarding an applicant's race, gender, or socioeconomic background.
"There's so much more to a person than what they write in an essay," said Sidney D'Mello, CU Boulder professor and the study co-author, to Daily Camera. "I really think these tools can help surface aspirational, deserving, amazing students who otherwise get overlooked."
Also: How ChatGPT (and other AI chatbots) can help you write an essay
Even though the AI tool can give unbiased feedback, it is not meant to replace human admissions officers.
Instead, D'Mello shared that the tool is meant to supplement the admissions officers' inferences, highlighting the applicants' character traits from their essays that the officer may have overlooked in search of an excellently written essay.
As with any AI model, there are present risks, and the researchers reassured the public that before the tools are used, additional work is needed to ensure accuracy and efficiency.
Shield AI Raises Valuation to $2.8B with New Funding January 3, 2024 by Ali Azhar
Shield AI's V-BAT
Shield AI, a defense technology company, raised its valuation to $2.8B with additional capital in Series F funding, which now stands at an impressive $500M. The tech startup is aiming to build the world’s first AI pilot for the U.S. military and its allies. The new funding will take it closer to achieving this goal.
According to Ryan Tseng, Shield AI’s CEO and Co-Founder, AI pilots are “becoming a strategic conventional deterrent in class with our aircraft carriers and guided missile submarines”.
Interestingly, this is the first software-defined strategic deterrent, which has only been possible with the recent advances in AI technology and compute power. Tsend believes that the AI pilot would be a huge paradigm shift for the aerospace and defense industry.
The new funding for Shield AI includes equity and debt. There is $100 million in equity raised at Series F price and $200 million in debt. The debt provider is Hercules Capital, while the identity of the equity provider has not been revealed by Shield AI.
The flagship product of Shield AI, Hivemind, is designed to enable teams of intelligent aircraft to execute missions autonomously in high-threat environments, without relying on GPS, waypoints, or remote operators. The use case for Hivemind includes everything from penetrating air defense systems to dogfighting F-16s.
“The defense and investment communities are seeing the profound impact AI pilots will have on national security and global stability. AI pilots solve the electronic warfare (GPS- and communications-jamming) problem that’s devastating 10,000 drones per month in the Russia-Ukraine War, and they enable the operating concept of intelligent, affordable mass, where swarms of affordable aircraft can accomplish missions normally reserved for expensive, exquisite aircraft,” said Brandon Tseng, Shield AI’s President, Cofounder, and former Navy SEAL.
The aircraft agnostic autonomy stack behind Hivemind is similar to the self-driving technology used for autonomous vehicles. Shield AI has already completed extensive flight hours with Hivemind on quadcopters and F-16s. The startup has more total autonomous flight hours than any other company in the world.
Venture debt often gets a bad rap, but if the capital is used strategically it could be exactly what is needed by late-stage companies such as Shield AI. The injection of new funding offers startups to access additional capital to accelerate growth without sacrificing equity. If Shield AI is successful in its goal to build an AI pilot that turns aircrafts into autonomous systems, it would usher in a new era for the aerospace and defense industry.
Shield AI recently launched its V-BAT teams that operate and complete missions autonomously. V-BAT is a type of reconnaissance unmanned aerial vehicle with the ability to land and take off vertically.
In his recent testimony before the U.S. Sense, Brandon Tseng emphasized the importance of AI pilot technology in the country’s overall deterrence strategy. According to Brandon Tseng, AI-piloted systems “will be the greatest military deterrent of our generation." However, he admitted that incorporating AI-systems in the Department of Defense has been challenging and urged the DoD to be more receptive to the “next game-changing technological assets."
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About the author: Alex Woodie
Alex Woodie has written about IT as a technology journalist for more than a decade. He brings extensive experience from the IBM midrange marketplace, including topics such as servers, ERP applications, programming, databases, security, high availability, storage, business intelligence, cloud, and mobile enablement. He resides in the San Diego area.
Anyone who's ever bought a car might find it hard to feel sympathy towards a car dealership, but you just gotta feel it for the folks at one Chevy dealership who fielded a version of ChatGPT to potential car buyers. As reported in Inc., Yahoo Finance, and Driving.ca, among others, the Chevrolet dealership in Watsonville, CA created a custom chatbot that quickly went off the rails.
Users were variously able to convince the chatbot to offer a 2024 Chevy Tahoe (roughly a $55,000 truck) for a buck, get it to recommend buying from Ford instead of Chevy, and write limericks that sang the praises of the Toyota Tundra (another Chevy competitor).
Also: Here's how to create your own custom chatbots using ChatGPT
In a statement in Driver.ca attributed to "Chevrolet" (we don't know if it's the main company or the dealership doing damage control), the company said:
The recent advancements in generative AI are creating incredible opportunities to rethink business processes at GM, our dealer networks and beyond. We certainly appreciate how chatbots can offer answers that create interest when given a variety of prompts, but it's also a good reminder of the importance of human intelligence and analysis with AI-generated content.
See what I mean? Somebody definitely had a very bad day. But, in the interests of making sure you don't have a similarly bad day, we've compiled a list of 20 things you should consider before opening up an AI to your customers.
Also: Bill Gates predicts a 'massive technology boom' from AI coming soon
I'm not going to cover the technical machinations required to make the AI comply with these guidelines, because that will differ from implementation to implementation. But everything I am suggesting is doable, either via AI APIs or with specific tools meant to build custom chatbots.
Let's get started, shall we?
1. Set boundaries for the types of queries the AI can handle
Compared to old-school expert systems, which were trained on specific sets of information, most AI chatbots use unsupervised training, which means they can access just about anything. Don't allow that. Only train your AI on context-specific information, and then limit the kinds of answers it can provide.
2. Conduct thorough testing in a controlled environment before public release
Don't just let your employees test this out. Bring in friends and ask them to go wild with what they ask. Test, test some more, and then test again.
3. Evaluate the AI's ability to handle complex or sensitive issues
Make sure you carefully examine the results of preliminary testing and make any changes that prove to be required. But don't limit your testing to simple trials. Be sure to push the limits of the AI and see how it responds to edge cases. This is how you can develop guard rails that keep the AI from straying into dangerous territory.
4. Plan for phased rollouts to manage customer expectations and feedback
You're still not going to find all the flaws, despite your testing. When you roll out your AI, limit it to controlled groups. But be careful: don't just limit it to compliant clients. Find a high school class and let them run amok in your AI, and watch what it does. Get a few curious and mischievous outsiders and ask them to find flaws. Then roll out to another small group and see if you get any weird behavior. Do it slowly, perhaps by invitation only.
5. Prepare for unexpected user interactions and queries
Even with all of that, you'll get unexpected results. You'll need a way to implement small improvements as you see how the AI performs. Make sure the developers are on board for continuous improvement, and also be sure you log enough information to allow the developers to trace any unplanned behavior.
6. Prepare a contingency plan for AI failures or errors
Create an escalation and rapid response plan if the AI starts to go off the rails. Make sure you have a way to quickly turn it off, and then a way to escalate the details to the developers to fix what went wrong.
7. Create a concise terms-of-service and mandatory clickwrap agreement for user acceptance
This can be presented as part of an account creation process, before the AI interface is ever provided to the user. It allows you (and your lawyers) to specify the legal bounds of the experience and disavow any unexpected behaviors or promises made by the AI. It is your posterior protection plan, just in case the AI runs amok as it did for Chevrolet of Watsonville when it tried to sell a truck for a buck.
8. Continuously evaluate and improve the AI's performance
Make sure you build monitoring into your process. You might want to be sure a human audits each interaction the AI has with customers, so that you're able to understand just what your customers are saying and being told. Make sure you have a way to provide input to the developers so improvements can be rapidly deployed.
9. Customize the AI to reflect brand voice and values
Your company (or, at least, the manufacturer whose products you sell) is likely to have very carefully defined brand identity guidelines. Make sure the AI follows those guidelines. Be sure to train the staffers doing the monitoring on brand guidelines so that they can also monitor AI activity from a brand identity perspective.
10. Monitor the AI for unintended biases or discriminatory behavior
Similarly, train your monitoring staff on how to identify biases and discriminatory behavior. As part of the regular monitoring and auditing process, look out for this behavior and escalate to the devs anything that needs to be fixed. Also keep in mind that if the behavior is particularly egregious, this is a way your staff can reach out to the customer or prospect in question to do damage control before a situation might escalate.
11. Be transparent with customers about the use of AI
Make sure you're clear to customers when they're interacting with an AI. This will both help you set expectations and give you a bit of an "out" if the AI doesn't respond as expected. Explain that the AI is an experimental feature or a bonus service offering.
12. Assess the AI's impact on customer satisfaction regularly
Some customers will be excited to try out the new AI features, but some customers will find it either weird or dehumanizing. Be sure to show sensitivity to these variations in responses, and give those customers who don't want to use the AI an alternative channel for getting help. In fact…
13. Provide an easy option for customers to reach human support
You don't want customers to feel they're trapped in some sort of AI bot purgatory. Make sure there's a clear and easy path for humans to reach other humans, whether that's by phone or text interaction. And, whatever you do, don't tell people they're talking to a person when they're actually talking to a bot.
14. Implement a feedback mechanism for customers to report issues or suggestions
Even customers who think the AI experience is cool may have some feedback, suggestions, or complaints. Provide an easy way to gather that feedback as part of the AI interaction. ChatGPT, for example, has a thumbs up/thumbs down button after each AI response, along with a way for users to provide more details about why they gave the answer that rating.
15. Assess and adjust the AI's tone and communication style based on customer feedback
An AI is capable of tuning its communication style by modifying factors like formality, friendliness, complexity of vocabulary, and overall tone. This encompasses not just the words chosen, but also the style and approach of communication – whether it's more casual or professional, straightforward or elaborate, empathetic or objective. Based on feedback from your customers, modify the AI's manner of speaking to best meet what they are most comfortable with.
16. Develop protocols for handling sensitive customer information via AI
We're all very aware of the need for security, especially as more and more criminals attempt to hack, or succeed in hacking, businesses. Information gathered by the AI must be protected. Carefully develop security protocols to make sure the AI asks as few questions as possible when requesting personal information, and then protects whatever information it gathers.
17. Train staff on how to work alongside the AI system
We talked previously about assigning staff to continuously audit AI responses, but didn't specifically call out training. Not only will the folks doing the audits need to be fully trained, but your whole staff will require training on how to use the AI, its limits, how to spot problems, how to escalate when serious problems are found, and how to communicate expectations and limits to customers. Ideally, training will be a continual process, with introductory training and regular refreshers as the technology evolves.
18. Consider the AI's impact on employment and staff roles
Undoubtedly, the introduction of AI customer service solutions will concern members of your team about their long-term job security. This is where you need to be fully transparent with employees, set expectations, and keep in mind you're dealing with real people with responsibilities, families, and feelings. Be sure to read ZDNET's Special Report, The Future of AI, Jobs, and Automation, for some very in-depth coverage and analysis of this complex issue.
19. Regularly update and maintain the AI system
AI, particularly generative AI, is evolving at warp speed. Be sure to plan for regular maintenance and updates of your AI systems. Something that's cutting edge in January could well be three generations behind by June.
20. Ensure compliance with relevant laws and regulations
Each industry is different, so this is one you're going to have to research based on your business type and location. Be sure to check with your attorneys to be sure your AI efforts (and the responses elicited from the AI) are within the bounds of your legal requirements.
Also: Have 10 hours? IBM will train you in AI fundamentals — for free
Whew! Well, there you go. Twenty things to consider before allowing your customers to encounter your AI. If you follow these guidelines, you can avoid many of the troubles that beset Chevrolet of Watsonville.
What do you think? Did I leave anything out? Are you deploying an AI in your business? Do you have any interesting stories to tell? Let us know in the comments below.
You can follow my day-to-day project updates on social media. Be sure to subscribe to my weekly update newsletter on Substack, and follow me on Twitter at @DavidGewirtz, on Facebook at Facebook.com/DavidGewirtz, on Instagram at Instagram.com/DavidGewirtz, and on YouTube at YouTube.com/DavidGewirtzTV.
Intel Launches Enterprise GenAI Company: Articul8 January 3, 2024 by Ali Azhar
Intel and DigitalBridge, a global investment firm, launched an independent generative artificial intelligence (GenAI) company named Articul8. The platform offers enterprise customers a full-stack, vertically-optimized, secure GenAI platform. Customers have the flexibility to choose between cloud, on-premises, or hybrid deployment options.
Articul8 offers a turnkey solution for enterprise customers looking to simplify their workflows while keeping data within the enterprise security perimeter. Articul8 is powered by Intel Xeon Scalable processors and Intel Gaudi accelerators. While the platform is built on Intel hardware, it does support a wide range of hybrid infrastructure alternatives.
“With its deep AI and HPC domain knowledge and enterprise-grade GenAI deployments, Articul8 is well positioned to deliver tangible business outcomes for Intel and our broader ecosystem of customers and partners. As Intel accelerates AI everywhere, we look forward to our continued collaboration with Articul8," said Pat Gelsinger, Intel CEO.
Intel technology is being used by several tech companies including Stability AI – the company behind the Stable Diffusion text-to-image model and Boston Consulting Group (BCG) — a leading global management consulting firm. One of the reasons why companies prefer using Intel technology to power their AI systems is Intel’s proven record of working with the open ecosystem and independent software vendors to scale technologies.
(everything possible/Shutterstock)
DigitalBridge Ventures is the lead investor in Articul8. Other investors include Fin Capital, Mindset Ventures, Communitas Capital, GiantLeap Capital, GS Futures, and Zain Group.
The diverse consortium of investors highlights the broad support and interest in Articul8. Intel and DigitalBridge will remain strategically aligned on go-to-market opportunities and collaborate on driving GenAI adoption with customers.
Articul8 has already been deployed in several industries including financial services and telecommunications, demonstrating its capability to meet enterprise needs including integration with existing tech stack and high security.
According to Marc Ganzi, the CEO of DigitalBridge, “every global enterprise is challenged to integrate GenAI capabilities into their workflows” and Articul8 can make it easy for them to achieve this. The easy-to-deploy and scalable nature of Articul8 empowers enterprises to unlock value from their proprietary data.
The collaboration of DigitalBridge and Intel began nearly two decades ago when DigitalBridge was still in the incubation stage at Intel. With the support of industry investment, DigitalBridge and Intel can continue their collaboration to accelerate Articul8's go-to-market strategy and scale its product offerings for the broader GenAI ecosystem.
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While artificial intelligence has been used to upscale older content to current standards, it hasn't yet found much of a place in the television sector on a large scale. That could be changing soon: LG has announced that its newest round of TVs will be using an AI-powered processor to provide a serious boost in quality.
LG will be using its new Alpha 11 processor in two sets at first, the G4 and the M4.
Also: The best TVs from Samsung, LG, Sony, and TCL compared
A press release noted that the new processor would have "4-fold higher AI performance" that would in turn provide "a 70 percent improvement in graphic performance and a 30 percent faster processing speed."
What does that mean? It starts with what LG calls "precise pixel-level analysis" to sharpen backgrounds. From there, the company said, the processor will employ AI to analyze frequently used shades and refine colors to match moods and emotional elements intended by content creators.
In short, your TV can understand what a show or movie's creator had in mind and adjust the display accordingly.
The processor will also use what's known as Dynamic Tone Mapping to fine-tune contrast by analyzing where light is entering a scene, creating an almost 3D look.
It's worth noting that the new AI features can be disabled if you're a purist, but will likely be turned on by default.
The G4's predecessor, the G3, already had what was arguably the best picture quality of the year and overcame one of the biggest problems facing OLED displays – bright rooms. If the company's new processor holds up to claims, it's easy to see the G4 taking the top spot again.
Also: The best picks for 32-inch TVs: Top small TVs compared
An AI-assist is also coming to the audio side of things, LG said. AI Sound Pro uses virtual 11.1.2 surround sound to separate dialogue from the rest of the soundtrack and to provide a richer and fuller audio experience without the need for an additional sound bar.
Another addition to this year's lineup is a little smaller. Last year's LG M3, which introduced the wireless Zero Connect Box that eliminated all cable connections, was only available in 77-inch, 83-inch, and 97-inch sizes. The M4 however is adding a 65-inch screen, bringing the super-fancy wire-free technology to a more modestly sized set.
Both the LG G4 and the LG M4 should be on display at next week's CES.
Artificial Intelligence (AI) has been around for many decades but now it has become a buzzword even among non-technical people because of the generative AI models like ChatGPT, Bard, Scribe, Claude, DALL·E 2, and a lot more. AI has moved beyond its sci-fi origins to reality, creating human-like content and powering self-driving cars. However, despite having extraordinary potential, irresponsible use of AI can lead to bias, discrimination, infringements on privacy, and other societal harms.
Considering rising ethical concerns and other potential risks posed by AI-generated content, many governments, including the Biden administration and the European Union, are establishing guidelines and frameworks to ensure the safe and responsible development and use of AI applications. Here we will discuss ethical issues raised by generative AI models and some proven solutions to them.
Ethical concerns raised by gen AI models
The evolution of generative AI has led to a rapid rise in the number of lawsuits related to the development and use of these applications. Here are a few critical ethical concerns influenced by AI technology.
Societal bias and discrimination
The content generated by AI models is as good as the training data. As a result, outcomes produced by models trained on poor-quality training data can be biased and discriminatory, and instigate public backlash, costly legal battles, and brand damage.
A report published in Bloomberg reveals widespread gender and racial biases in around 8,000 occupational images created by three popular AI applications viz. Stable Diffusion, Midjourney, and DALL-E 2.
Deepfakes
AI tools can be used to create convincing image, audio, and video hoaxes. The content created by sophisticated models is often indistinguishable from the real one. Deepfakes are being used to spread hate speech, mislead people, and distort public opinion.
Copyright issues
Generative AI applications trained on data scraped from online sources have been accused of copyright and intellectual property infringement.
AI Regulations
Many governments, including the European Union(EU) and the Biden administration, have proposed regulatory frameworks for artificial intelligence.
EU AI regulations: The bill proposed by the European Union to regulate the use of AI underscores guardrails by enforcement agencies on the adoption of AI applications within the EU countries, restrictions on AI use for user manipulation, and limitations for the use of biometric identification tools. Consumers can file complaints against any violation or invasion of their privacy. The law also proposes financial penalties of up to EUR 35 million or 7% of a company’s global turnover for non-compliance with the regulations.
White House Executive Order on AI: The Executive Order (or EO) on AI issued by US President Biden focuses on the safe, secure, and reliable development and use of AI tools. New standards for responsible adoption of AI have been outlined in the order, along with guidelines for the protection of intellectual property and user privacy.
Strategies for a Safe and Secure AI Application
Following are some strategies to mitigate the ethical and security challenges of AI applications.
External audits: Companies building AI models need to work in partnership with an AI data solutions company, like Cogito Tech, for external audits. Cogito’s Red teaming service offers adversarial testing, vulnerability analysis, bias auditing, and response refinement solutions.
Licensed training data: Licensed training data can ward off copyright and intellectual property infringement issues. Licensed data is procured through legal process in compliance with copyright laws. Cogito offers DataSum service to address ethical challenges in AI for complex data governance and compliance needs.
Final words
Artificial intelligence, especially generative AI, has revolutionized the way we interact with technology in the last couple of years. It holds extraordinary potential to make businesses more productive, innovative, and secure. However, misuse of AI or a data-biased AI model can trigger an array of ethical and security concerns including bias, discrimination, breach of copyright and privacy, disinformation, and even pose risks to national security.
It is crucial to recognize and address these challenges to harness AI for good and realize its great benefits.
About the author: Alex Woodie 
