Gather AI, a company co-founded by an award-winning team of three robotics engineers from Carnegie Mellon University with a combined experience of 25 years in developing autonomous flying vehicles is disrupting the supply chain industry

Gather AI

Practically 95% of any human reaction is a form of prediction. From how one gets dressed to the chosen commute, every decision is guesswork based on parameters such as the weather, traffic, and time of day. Yet, as inventory forecasters predict demand on multiple levels, the number of external factors impacting their decisions also skyrocket, making it extremely difficult for humans to make the predictions. Robust inventory management is the unsung hero in logistics—and it’s encouraging the global supply chain AI market to reach by 2028. But it’s dependent on one thing: Accurate forecasting. $17.5 billion providers and data teams today need to be hot on understanding the , what sources they require to meet their objectives, and how to maintain its quality. Logistics power of data With that in mind, let’s explore how paying more attention to data quality and advanced analytics can optimize inventory forecasting and improve operational efficiency. Why Accurate Data is Essential in Forecasting McKinsey reports that applying AI-driven forecasting to supply chains reduces errors between . But the limited usefulness of the data collected remains the top barrier to successful implementation, keeping the rate of AI adoption since 2018 at a low 6%. With the right data and tools, inventory forecasters can predict anything from how much time they have to release trending products to the risks and costs of missing out on the opportunity. 20% and 50% Take fast-fashion brands, for example. They distribute up to 20,000 clothing stock-keeping units (SKUs) across ten countries in the EU, where price sensitivity and sizes vary per location. Then the new Barbie movie is released, and suddenly everyone wants to wear pink. That’s over 200,000 SKUs to consider . barbie-fying While stores like Gap are ahead of the game in creating a , others are apprehensive, knowing that the merchandise will end up on sale if movie ratings are low. Gap x Barbie collection Inventory forecasters need to weigh everything from budget to the predictability of movie success—and they want these figures yesterday. With accurate data regarding operations, such as delivery speed, workers available, and vehicle capacity, alongside external inputs, including consumer trends, AI-powered analytics tools can help inventory teams make data-driven decisions instantly. AI for Data Collection and Storage Many organizations possess sufficient data to leverage the benefits of AI-driven forecasting: A quality forecast could be achieved on 80% internal and 20% external data. The trick is ensuring that data is collected, stored, and visible to inventory teams. And this truth is something data champions like and are more than aware of. For stores to better support customer fulfillment and omnichannel retailing, the two companies have started putting drones to work as a method of tracking warehouse inventory. Gather AI IFM From counting parcels to spotting at checkout, computer vision AI technology tools can detect and track objects and SKUs as they move through the warehouse or store. This helps provide the continuous and reliable updates needed for making real-time data-driven decisions while removing room for human error. retail theft Automating data collection, such as the number of items on shelves and the locations of particular pallets, helps inventory teams understand their operations and needs in real time. While storing operational, financial, and external data required to meet inventory objectives in central repositories enables data teams to capture a holistic view. That’s why retailers and companies must devote more time to their data collection and storage methods, looking at how they can increase data visibility. This includes ensuring strict access controls and encryptions to protect data and keep it in the hands of those with authorization. logistics AI for Data-Driven Inventory Forecasting At the analytical stage of the data lifecycle, AI forecasting tools, fed with continuous and reliable data, can predict sales down to the SKU level. This way, sellers can adjust production levels, manage procurement, and allocate resources effectively. This alignment of inventory with demand reduces handling and storage costs and improves overall operational and financial efficiency. Here’s how to implement forecasting models with success: What business changes could affect the inventory variables? What product expiration periods, storage space, and storage conditions are required? How could staff turnover and ship maintenance affect this? Defining reasonable ranges for these parameters is crucial to avoid creating unrealistic outputs leading to adverse business decisions. Define the critical parameters: Unexpected events will likely skew AI predictions. The smoothing technique is a statistical approach to eliminating outliers from datasets. It helps to minimize the variation in data between periods where an anomaly occurred, creating a more representative data set for AI-powered forecasting models to learn from. Apply data smoothing: Take Covid-19, for instance. The data captured during this time falsely illustrate business as usual and would require smoothing. If data is incomplete, logistics teams struggle to have an accurate historical representation, and assumptions go awry. For this reason, that is ‘synthesized’ or ‘artificial’ information generated from existing data—is used to train AI-powered forecasting models with potential scenarios. The machine can define more data classifications and increase predictability by reading a broader data set. Train what-if simulations with augmented data: augmented data— What-if scenarios are critical in cases of limited data samples, which diminishes confidence in forecasting with accuracy. They are also valuable for addressing distant future uncertainty where it is challenging to predict unexpected events. Depending on the size of your data, you may choose a simpler AI model or a more complex one where larger data sets and many iterations are available to train numerous parameters. Harnessing the power of data-driven inventory forecasting is the key to unlocking logistics companies’ full potential in today’s complex supply chain landscape. With robust forecasting models, inventory managers can ensure the right products are available when customers need them, leading to enhanced customer satisfaction, loyalty, and a competitive edge. Organizations can go on to assess market opportunities, plan new product launches, manage product lifecycles, and make optimal supply chain decisions aligned with business objectives. This article was originally published by Hristo Hadjitchonev on . 150sec

From Instinct to Insights: Data-Driven Inventory Forecasting in Logistics

The use of Facial recognition and other AI technology in video surveillance is revolutionary for law enforcement agencies. You don’t need to go through hours of footage to look for one particular person. Through facial recognition and identification, you can jump to the exact spot in a video where the person appeared. However, there’s one challenge – compliance! Are law enforcement agencies allowed to use facial recognition, as it infringes upon the privacy of citizens? If yes, then how and when can they use them? The showed through a survey that 75 out of 176 countries studied were actively using AI for video surveillance. AI allows them to analyze large amounts of recorded footage as well as analyze footage in real-time to reduce human error. Law enforcement agencies can track criminals, automatically detect crimes, and respond to them swiftly. Carnegie Endowment Compliance Requirements for Use of Facial Recognition Technology in US and EU Law enforcement officers usually need to go through hours of recorded surveillance footage to identify a particular suspect and gather evidence. AI can help speed up this process and tell them exactly where the suspect appeared. USA In the US, the use of AI facial recognition technology to save time in finding a suspect is allowed under the . Under this act, law enforcement officers need to obtain a warrant, with a probable cause of suspected criminal activity. After obtaining a warrant they can use facial recognition technology only for 30 days after which they need to get the period extended. Facial Recognition Technology Warrant Act However, law enforcement agencies need to be careful as the same act requires them to minimize the acquisition, retention, and dissemination of facial recognition data outside of the warrant’s purview. This means that facial recognition only needs to be run on the suspect and faces of other people occurring within the video need to be redacted. EU In the EU, facial recognition technology is governed by GDPR laws. As per of GDPR, facial recognition data is considered as sensitive data and processing such data is prohibited. However, there are certain special circumstances, when such data can be used and one of them is “for the establishment, exercise or defense of legal claims”. Facial recognition data can also be processed for criminal offenses. This means that law enforcement agencies or lawyers can use such AI technology to look for a certain person among large amounts of footage. Article 9 Moreover, GDPR allows use of facial recognition if consent has been given by the person whose face is to be identified. However, consent under GDPR does not simply mean that the person has said “yes” to such usage. Pre-ticked boxes do not count as valid consent. The person also has the option to withdraw such consent at any point in time. Lastly, under GDRP, organizations using facial recognition technology need to carry out a Data Protection Impact Assessment, where they identify and reduce risks related to the use of such technology. If this assessment is not done, then according to , organizations using AI facial recognition can be fined up to €10 million or 2% of the organization’s global turnover; whichever is higher. Article 83(4) Do Video Platforms Use AI Facial Recognition in a Compliant Manner? There are many other compliances related to the use of AI technology that further complicate the matter. These include compliance regarding how data is stored and processed. Thankfully, there are advanced video platforms that allow organizations to use such technology without having to worry about compliances. Panopto, Kaltura and VIDIZMO are examples of video platforms that allow you to host surveillance videos in a compliant manner. Panopto is hosted on AWS Cloud, which covers numerous compliances that include PCI-DSS, HIPAA/HITECH, FedRAMP, GDPR, FIPS 140-2 and much more. is hosted on Azure Cloud, which covers more than 90 compliances in multiple geographies. VIDIZMO even provides facial recognition technology as a feature so organizations wouldn’t have to worry about procuring such technology from third-party providers. VIDIZMO The underlying benefit of using a video platform is that organizations do not need to worry about multiple compliances with regard to the storage and handling of data. The cloud providers do this for them. Organizations only need to obtain a warrant or explicit consent, and facial recognition technology can be used. I hope this blog helped you understand the various compliances around facial recognition technology and how your organization can use such technology. You can read up more through the articles and acts linked in this blog.

AI Compliance Requirements: Are You Allowed to Use Facial Recognition?

With outdated dystopian movies like making headlines for possibly predicting the future and with companies like Google already releasing Artificial Intelligence (AI) tools and bots, is it time to rethink the Terminator narrative. As we move towards a world that is increasingly digitized, we must work to truly understand what it means to have and use AI. Terminator How does AI work? Traces of AI have appeared in mythology, literature, scriptures, and almost everywhere else you wouldn't expect before it did in academic discussions!  For centuries many different cultures put forth their versions of AI but it wasn't until the late 1950s the idea was formally theorized. All of Artificial Intelligence is based on a single underlying assumption that all human thoughts can be mechanized. While the way humans learn isn't a single dimensional process, almost all of it is emulatable by a machine. To make this analogy a little clearer, I shall look at intelligence (both human and machine) as a toolbox of skills. The most basic of this is . Even the most unintelligent of animals and organisms use information through sound, vision, and other senses to react to the external world appropriately. For humans, it is no different and is typically something that comes innately to us. However, this makes it all the harder for machines to replicate. This idea is known as which states that contrary to assumptions and traditional reasoning, the sensorimotor skills which come so naturally to humans require enormous computational resources for motions to replicate. sensory learning Moravec's paradox As a result, we have made strides in , , , and similar sensory-based learning technologies only in the last 3-5 years. Although now, these technologies are just as adept (if not better) at recording sensory input. computer vision robotics tactile signaling AI and GPU Advancements A slightly more advanced tool that not all 'intelligent' creatures possess is the ability to . In the past this was the biggest challenge for AI. gather, store and analyze information Training a model (the technology that 'teaches' AI how to behave) needs huge computational power. This was an obstacle for quite a few decades until about 6-8 years ago when GPUs were put to use for purposes beyond graphics processing. With the parallel processing power it offered at a relatively low-cost, machines were finally able to crunch a lot more numbers, overcoming computational difficulties that we struggled with in the past. Machine Learning NLP, Neural Networks and Big Data To make the whole process a little more natural, supporting technologies like and were developed to convert sensory and other inputs into a serviceable format. speech recognition natural language processing Then developments in and helped support the machine learning models that were being put in place for information interpretation. neural networks big data Finally, with these technologies under our belt, we are able to build fairly reliable AI for particular context-specific use cases. This is known as . Most current applications fall under this category. narrow AI If we look to scale things further, we must look at the most powerful skill in our human intelligence tool kit: our ability of . While AI hasn't fully gotten there yet, cutting-edge research about and is being done to make AI more useful for a broader range of tasks. abstraction transfer-learning meta-learning With this, AI will be able to reproduce humanity's extraordinary ability to generalize learning from one situation and apply it in a different context. If this is achieved, we may finally get to the AI that the franchise predicts: (Artificial General Intelligence). This form of AI can essentially mimic all intellectual human activities and eventually will be able to supersede human abilities. The earliest estimates for this, if even possible, are at least 10 years out. Terminator AGI Bottom Line It has been made abundantly clear by our art and pop culture that we think AI is a distressing technology to be worried about. Whether or not this is true, wherever there is data, there will be AI and I believe that it will be the backbone to the technologies that will govern our future. While my naivety would like for me to think that AI will lead to a Utopian world with better healthcare, self-driving cars and more, if current trends are indicative of anything then this will push the world towards more disparity. While it is undeniable that AI is an empowering technology, it is also key to note that the people who are profiting off of its development are the one-percent. They will continue to do so, driving further power, income, and social inequality. Therefore, it is inaccurate and unlikely that the conflict between AI and humans would even occur. Rather, the more real conflict to be worried about is the one between humans who have AI at their fingertips and those who don't. The narrative from the Terminator is rather misleading (admittedly it was a movie released nearly 40 years ago!). The more likely reality will be that AI further aggravates the dichotomy between the haves and the have-nots. Previously published on: https://lucidityproject.home.blog/2020/07/15/artificial-intelligence-intro/

Artificial Intelligence: Time to Terminate the Terminator Tale?

Artificial intellige­nce that can generate­ content is quickly changing the way search e­ngine optimization (SEO) works. These new-age language models and machine­ learning systems can study huge amounts of information. The­y can also comprehend context and me­aning and produce content that sounds like a human wrote­ it. This creates brand-new opportunitie­s for SEO professionals to simplify processes, gain valuable­ insights, anticipate future trends, and craft more­ relevant content optimize­d for search engines. Here are some of the top use cases where generative AI can elevate your SEO strategy: Improving Site Architecture and Internal Linking An intuitive site structure with an optimized internal linking strategy is key for helping search engines understand your content topics and hierarchy. Generative AI can examine your existing content inventory and user search data to extract a clear logical taxonomy and category structure that aligns with user intent throughout the buyer journey. AI technology scans use­rs' browsing habits and queries to build a navigation system that's intuitive­ and customized. This enhanced, e­asy-to-use experie­nce boosts those all-important user e­ngagement numbers which he­lp sites rank better. No more­ frustrating, hit-or-miss site navigation — instead, a streamlined journey thanks to artificial intelligence­ analyzing usage trends. Conducting Smarter Keyword Research Rather than just looking at keyword volumes, generative AI enables more intelligent and contextual keyword research by understanding relevance and search intent. You can try using Bing Chat from Microsoft! It's an amazing tool that lets you prete­nd to be a customer looking for information. You can ask all sorts of questions just like­ a real person would. Then, Bing Chat will show you the­ top search results for those que­stions. It works for all kinds of searches, whethe­r you're trying to find products to buy online or just looking for gene­ral info. But that's not all! The really cool part is that this AI tool can look at all the top re­sults and find patterns. It can see what topics ke­ep coming up, what types of content are­ most popular, and how people use ce­rtain keywords in different situations. With this information, you can cre­ate a content strategy that give­s your audience exactly what they want to read or watch! Instead of just following whateve­r was hot yesterday, you'll know the ne­west trends before­ anyone else. Identifying Opportunities for Rich Results Using schema markup to qualify for SERP features like FAQ-rich results, HowTo instructions, product listings, and more can significantly increase your visibility and click-through rates from universal results. Generative AI assistants can review your content catalog and suggest relevant rich data opportunities. They can then generate the precise schema code required, improving technical SEO implementation. Tools like ChatGPT can also analyze your top-ranking pages and suggest formatting tweaks like adding section links or rewriting introductory text to increase your chances of earning coveted featured snippet placements. Analyzing Sentiment and Off-Site Signals In the age of prioritizing E-E-A-T (experience, expertise, authoritativeness, and trust), monitoring brand sentiment and capitalizing on off-site endorsement signals are crucial for establishing authority. AI can gather data about companies and ite­ms from websites and social platforms. It looks at comments, both good and bad, and sugge­sts ideas. This helps improve products, plan conte­nt, and build a brand that gets top spots on search engines. Enriching Content and Persona Alignment Creating content that truly connects with your audience requires understanding their emotional needs, interests, and inherent preferences. Generative AI assistants like Claude can analyze data sources like customer surveys, forum discussions, reviews, and more to extract deep insights into your key personas. It can identify emotional triggers, underlying needs, commonly used vocabulary, and other psychological clustering signals that allow you to refine your content strategy and creative process. This produces more resonant, emotionally intelligent content that helps in  engagement and meets the E-E-A-T criteria of being truly valuable and high-quality for your intended audience. Predictive Modeling of Trends and Opportunities Instead of reacting after the fact when a trend emerges, generative AI enables predictive content planning. It can continuously monitor search data, news and social media sources, sales figures, and other real-time datasets to forecast growing topics, shifts in demand, product interest spikes, and other consumer behavior patterns before they become trends. This foresight provides a significant competitive advantage, allowing you to develop content and prepare inventory ahead of market movements to capitalize on nascent opportunities before your competitors. Automating Tedious Analysis and Reporting Many routine SEO tasks involved in data gathering, analysis, and reporting can be streamlined through generative AI assistants. AI tools can help you save­ time and work smarter by automating tedious tasks. From gathe­ring and analyzing data from analytics tools to creating visually appealing prese­ntations that showcase your SEO progress through charts and graphs, AI can streamline­ your workflow. You can bring AI assistants like ChatGPT into your web applications and project manage­ment software using APIs and no-code platforms. This allows you to automate­ repetitive tasks like­ generating regular status re­ports and insights, freeing up your time and e­nergy to focus on more strategic and important prioritie­s. Powering Advanced Content Generation While AI-generated creative assets are still not a complete replacement for skilled humans, generative models can increasingly aid the content development process as a force multiplier. AI assistants can draft outlines, generate section drafts, or propose ways to frame and structure content based on your parameters around tone, audience, word counts, and other guidelines. This augments creative workflows, with humans providing strategic oversight, subject matter expertise, and quality assurance while offloading some of the heavy lifting to AI co-pilots. Over time, the quality and sophistication of generative AI content will continue advancing. Suggesting Content Enrichments and Enhancements In addition to aiding with net new content development, generative AI can analyze your existing content catalog to surface areas for improvement regarding depth, freshness, comprehensiveness, and alignment with searcher needs. Whether suggesting additional sections to expand content coverage, proposing internal linking suggestions to improve information flow, or identifying stale material requiring updates or repurposing, AI assistants provide another set of eyes to ensure your content remains optimized in an ongoing capacity. Personalizing and Dynamically Updating Content For very large, complex websites, keeping content fresh and continuously optimized is a tough challenge. Enterprise domains can use generative AI's capability to personalize content in real-time based on user signals and contextual data. Perhaps an e-commerce site could incorporate a user's browsing history and geolocation data to dynamically adjust a product description's tone and highlight details when someone views that page. This level of granular, AI-powered content adaptation and personalization can drastically improve relevance levels compared to static content simultaneously served to all visitors. In Conclusion As these use cases illustrate, generative AI integration for SEO spans technical grounds like architecture and keyphrase strategy to creative fields like content development and multimedia asset generation. By using these powerful AI models' analytical prowess and their capacity for human-like content production, you can gain the ability to scale, accelerate, and elevate your SEO work to much more sophisticated and impactful levels.

Top Use Cases of Generative AI in SEO

Online gaming has evolved, and we are probably at the pivotal point where the future of gaming will include AI in game development, gameplay, and add-on tools. So many people spend their time in alternative worlds such as VR shows, games, etc. The gaming market is worth $546.99 billion and growing at 13.9%. However, another leeching industry arising from it is the AI-powered game hacks market, which is also growing while reducing the profit share of mainstream companies in the gaming industry because many players give up playing online with multiplayers. Online game cheaters and hackers have been a pain in the a$$, and that is about to change. Artificial Intelligence (AI) has shown tremendous capabilities in different fields such as cybersecurity, programming, software development, and even . Now they are in gaming, but with positive and negative effects based on how certain players use them. This article highlights the problems of the gaming industry as they pertain to AI influence and the future. the revolutionization of African languages AI Cheats in Gaming online gamers uses cheat codes. It gives them an unfair advantage to have the highest accomplishments in playing online multiplayer games. They are most common in first-person shooter (FPS) and massive multiplayer online (MMO) games. AI cheats are used in games such as Call of Duty, PUBG’s Battlegrounds, GTA 5, Destiny 2, Counter-Strike, and lots more. One out of three Cheat codes are programs developed to give certain players an edge through certain features like 'god mode', endurance, identifying enemy locations, and so on. They are on every system and console. With 33% (based on previously mentioned statistics) of players in any game being cheaters, the fun and thrill of the gaming experience are ruined for many sincere gamers. Although there is no formal data, I gather that the AI game cheat industry is worth hundreds of millions of dollars, edging into billions. Here are some numbers. , a gang was caught by the Chinese police and Tencent, with $76 million in revenue charged to clients as subscription fees. Another stated they know cheat developers who make over $2 million per month, accumulating to $24 million per year. Imagine more cheat developers, that would justify my claim. In 2021 report by Old School Gamers Types of AI-Powered Game Cheats Sometimes, developers insert cheat codes in games purposefully for the sake of debugging or identifying ways to improve the game and its experience. However, third-party software developers and gamers come up with malicious code that give players a fair advantage and exploit game companies. A primary way these codes work is to serve as a mediator between remote play and the game. It leverages the live stream of your game, as long as you are connected to the internet. It enables mouse and keyboard tracking on consoles to give you feedback that you can use to your advantage. Players also cheat with AI-aided aim to assist them. It is called ‘aimbots’, which use AI to read and find the enemies and send the data back to the cheater's system, whether Android, PC, Game Pass, etc. It aims the enemy with computer-enhanced precision while functioning as a remote control. Advanced AI game cheats “A new kind of aimbot has taken games like Warzone and Apex Legends by storm. It uses computer vision to identify enemy players in a complex game world and then signal the player to shoot at a specific location. “Players use a PC to process the video feed from their games console, and then run the automation and machine learning scripts; they would also connect the console’s controller to the PC via a hardware device, to emulate the controller's signals. Players can set the extent of aim assist, and even input the system to select specific target zones on enemy bodies – headshots included.”- AI Business While some basic cheats are detectable by anti-cheat programs, most are not (at least, not until recently). These advanced cheats have no capture card, no special device, are totally undetectable, and are easily accessible. They use live play to make aspersions, draw conclusions, and recommend actions. Systems from the referenced from AI Business above, analyze if an enemy is visible from a player's position and uses "Triggerbot" (another intelligence program) to fire at them. Some advanced systems like ESP use object analysis and image recognition to differentiate between a player and an object on the screen. This program is trained with numerous image textures to enable it to identify between an object and a player. There are cheats such that a cheater’s system learns the dynamics of players' movements to learn maps of unfamiliar territories and even adapt to how a human expert gamer would play a game and simulate the same pattern. automated or assisted play Current AI Anti-cheat Solutions and why They are Not the Future of Gaming Many have promised and failed, and some players are currently not enthusiastic about the possibility of a better solution. When announced its anti-cheat, it all looked hopeful until it was launched, and it had many false positives and wasn’t used beyond Battlefield 5. BattlEye was another that didn’t impress so much. It was designed to maintain fair and competitive environments within online multiplayer games. Developed by Valve Corporation, VAC employs advanced algorithms and detection mechanisms to identify and prevent cheating, hacking, and other unauthorized activities that can undermine the integrity of gameplay. Valve anti-cheat (VAC) By actively scanning for cheating software, modified game files, and suspicious behavior, VAC helps create a level playing field where players can compete based on skill and strategy rather than unfair advantages. The problem was that there were false positives and causes lag time for players. Although VAC is impressive, a solution befitting for the future of gaming has to be swift and not spoil the fun with false positives. FaceIT Anti-Cheat is used in the FaceIT competitive gaming platform. It combines client-side and server-side checks to ensure a cheat-free environment for competitive matches. Some users complained of ridiculous lag time or the failure for the app to launch. FaceIT Anti-Cheat: ESEA (E-Sports Entertainment Association) offers a subscription-based anti-cheat solution for Counter-Strike: Global Offensive. It focuses on protecting the integrity of competitive gameplay. Sadly, players may be able to create new accounts and be more careful with the way they play. ESEA Anti-Cheat: Source: Steam The Future of Gaming with the Perfect AI Anti-Cheat: Is there One? A perfect anti-cheat tool (if there's ever going to be any) isn’t just going to be a software but an AI tool that can study game plays, monitor accuracy, and judge the ratio of time spent online with the proficiency of a registered user, among many other things. It has to be a constantly learning and evolving tool that discovers each new gimmick that cheaters come up with. For the longest time, people have been looking for such, and that is where Anybrain step into the gaming foyer. : Anybrain In my research over the past few months, only Anybrain has the best prospects of tackling the issue of online cheat gaming. It is still in the testing stage, with lots of work ongoing. It is also an AI tool that analyzes multiple parameters in a player’s gameplay to detect irregularities that human eyes might be unable to perceive. It considers: The use of aimbots to target enemies The accuracy of players in shooting down enemies around obstacles Player behaviors (looking around for enemies before shooting, reaction time aiming, on-screen movement into enclosed territories). They seem to almost tick all the boxes in a research paper I found . The article emphasized mouse behavior, keystroke dynamics, and console logs. here about data capture for cheat detection systems What is Anybrain Contributing to the Future of Gaming? In its current operations and prospects, Anybrain creators have interesting plans. The solution is a machine learning program, so it is constantly learning based on data observed in how players play. In reviewing players in a game, Anybrain’s program pixelates the view of aims to detect if aims are aided or not. Due to its ML model, it gains accuracy over repetitive play. Observing repetitive play is important because sometimes a player might be good or just lucky, and it would be unfair to nuke them for the way they shoot down a target just once. It takes over 5 minutes for the program to detect a cheat with all these parameters in real-time. They are looking forward to implementing a biometric profile for players, and I believe this is the future of security among gamers to protect account integrity and limit cheating! A biometric profile will give the system a baseline of two hours to understand how a registered player plays. It is almost like gait recognition (using deep learning to collect better data, which is the act of tracking the unique way someone walks or runs to identify them even if you can’t see their face). Once detected, it brings a permanent ban on a player, and they don't have the opportunity to create another account to continue the game with the same biometric information. See video on  the problem of online gaming cheat and the bright future of gaming with the new AI anti-cheat. https://www.youtube.com/watch?v=LkmIItTrQP4&embedable=true Some folks think it is pointless, but I believe the future is bright and great, like this user that I found on . With AI leveraging numerous sources of data and speed, the learning curve will be exponential. Reddit Conclusion: It’s the dawn of a new era in online gaming. Those who have given up on online games can begin to dust their boots again. Soon, there will be more companies like Anybrain leveraging deep ML capabilities to train effective programs that will catch cheaters in multiplayer games. Of course, some gamers will keep trying to cheat, but the automated anti-cheat programs also won't be tired of learning and fishing them out. It's a new day, and the future of video games is bright with AI in it!

The Future of Gaming with New AI-Powered Anti-Cheats

El futuro de los juegos con nuevos antitrampas impulsados por IA

L'avenir du jeu avec de nouveaux anti-triches alimentés par l'IA

नए एआई-पावर्ड एंटी-चीट्स के साथ गेमिंग का भविष्य

AI を活用した新しいアンチチート機能によるゲームの未来

O futuro dos jogos com novos anti-cheats alimentados por IA

Tương lai của trò chơi với các tính năng chống gian lận mới được hỗ trợ bởi AI

全新人工智能反作弊游戏的未来

Artificial Intelligence (AI) is an umbrella term that covers and influences a wide range of tools and technologies. From machine learning to natural language processing, AI is changing how businesses operate and interact with their customers. Customer success teams are no exception. In fact, AI is already starting to play a role in customer success operations. This is possible due to the all-encompassing features of AI and its ability to approach consumer journeys from multiple points of view. Let's take a look at some of the ways AI is changing customer success and what this could mean for your business. What Is Customer Success? Definition and Key Stats Some companies struggle with customer success because it's a vague term that deals with many touchpoints. That's why it is important to define the concept before learning how AI is changing it. By definition, customer success means anticipating customer challenges or questions and proactively providing solutions and answers. It's a process that ensures your customers get maximum value from your product or service. Customer success involves different variables: Actively managing customer interactions Setting and tracking goals for each customer Measuring their success over time Customer success teams work with customers to make sure they're using the product correctly, troubleshooting any issues they have, and helping them achieve their desired results. This is critical because reports show that nearly are ready to pay for a better experience. That's also why companies that prioritize customer success generate than their competitors. 90% of buyers 60% higher profits 5 Ways AI Influences Customer Success Customer success is vital for your business, but what does AI have to do with it? It turns out that AI has started influencing customer success on many different levels, but we will show you the top five impact factors. AI Enables Social Listening AI-enabled social listening platforms allow businesses to glean insights from large volumes of social media data more quickly and effectively than humans alone could. By automatically identifying, parsing, and aggregating relevant data points from across all major social media platforms, these platforms provide businesses with an instantaneous overview of how their brand is being discussed online. AI-powered social listening helps businesses track and analyze online conversations about their brand, products, and services. This can provide valuable insights into customer sentiment, product popularity, and competitive intelligence. Businesses can use social listening data to better understand what customers want and need. It allows companies to identify potential areas for product innovation and gain a competitive edge by understanding what their competitors are doing wrong (or right). It Strengthens Customer Service Through Chatbots The use of AI in customer service has revolutionized the way businesses interact with their customers. are used by businesses to augment and automate customer service operations. AI-enabled chatbots One of the most significant advantages of using AI for customer service is that it allows businesses to automate repetitive tasks, which frees up customer service reps to provide more personalized support. Additionally, chatbots can collect data about customer interactions which can be used to improve product design and marketing efforts. Finally, the use of AI-enabled chatbots makes it possible for businesses to offer 24/7 support at a fraction of the cost of employing a human customer service team. AI Takes Data Analytics to a Higher Level Businesses use AI in customer-related data analytics to help identify patterns in consumer behavior. The goal is to use this information to improve customer engagement, loyalty, and retention. Besides that, AI provides opportunities to analyze customer data in ways that were not possible before. For example, natural language processing can be used to identify the sentiment of customer feedback, which can then be exploited to improve customer service. AI also makes it possible to process large amounts of data very quickly. This means that more data can be analyzed in less time, which leads to better insights and decisions. It's Possible to Create Personalized Offers Personalization is vital to the success of modern businesses. That's why AI comes in handy as it can lead to better decision-making and help create personalized offers for each consumer individually. AI can gather data about customers' preferences and past purchases. This data can then be used to create targeted offers that are likely to be of interest to individual customers. At the same time, AI is able to monitor customer interactions with websites and other digital channels. It represents a valuable source of information because it allows businesses to identify which products or services are most popular among their customers and what sorts of offers are most likely to result in sales. AI Enables Trend Prediction AI represents an extremely powerful tool for predicting trends in business. By analyzing large data sets, AI can identify patterns and trends that humans would never be able to see on their own. This can help businesses make strategic decisions about where to allocate resources, what products to develop, or even which markets to enter into. In other words, agile companies can prepare well in advance for coming trends and capitalize on them before their competitors do. And it's not just an option anymore - it's a necessity in order to stay ahead of the curve. Metrics That Can Prove AI Is Working People in business often say that you can't improve what you don't measure. This is exactly why we want to reveal the metrics that can tell you whether AI in customer success is really working or not. Here are the most important metrics you should keep an eye on: : If your revenue is increasing steadily quarter over quarter, it's a good indication that AI is helping you boost sales and . Revenue generation grow your business This metric will tell you how much you're spending to acquire new customers. If the number is decreasing, it means that AI is enabling you to attract more customers at a lower cost. Customer acquisition costs: : A low churn rate is always a good sign, and it's one of the most important metrics for customer success. Customer churn rate : This metric tells you how much each customer is worth to your business on average. If the CLTV is increasing, it means that AI is helping you bring in more valuable customers. Customer lifetime value : Measuring customer satisfaction is a must, and you can do it with surveys, , or even social media sentiment analysis. Customer satisfaction feedback tools : The NPS is a popular metric for measuring and satisfaction. Net Promoter Score customer loyalty If you track at least a few of these indicators, you will realize that AI is a precious customer success tool for your organization. Conclusion AI is changing customer success in a lot of ways. Some of the most important advantages include automating repetitive tasks, providing personalized support, taking data analytics to a higher level, and enabling trend prediction. Businesses that want to stay competitive need to make sure they are to its full potential in order to better understand their customers and create personalized offers. Are you ready to give it a try? using AI

5 Ways to Use AI to Improve Customer Success

AI has progressed throughout time and has far-reaching implications for most tech-driven businesses, including the insurance industry. Several insurance firms are to gain a competitive advantage in today's digital world. This has allowed them to deploy data modeling, predictive analysis, and across the whole insurance value chain, with positive results in terms of greater profitability and customer happiness. utilizing artificial intelligence (AI) machine learning Artificial Intelligence in the Insurance Industry Today, artificial intelligence (AI) has barely begun to scratch the surface of the insurance sector. AI has the ability to improve dozens of processes, and more insurance companies are likely to adopt the technology in the future. Here are a few of the most AI-friendly applications. Pricing Pricing is one of the most promising areas where AI might help the insurance sector. Insurance firms may use AI to price their policies more competitively and tailor them to each individual consumer. A home insurance company, for example, may utilize AI to gather data on a person's marital status, geographic area, and probability of filing a claim in order to establish premiums based on those factors. Claim Handling Insurance firms invest a lot of money in claims in the form of human management. Insurance premiums are frequently raised to account for case-solving costs. However, by automating many of the time-consuming operations associated with claims handling and payouts, an insurance firm may save money on recruiting. The time it takes to process claims would be cut in half. It would also allow insurers to offer consumers more tailored contracts based on their specific circumstances. A case study by was able to create a virtual assistant that handled 50% of the customer’s payment and information inquiries for an automobile and homeowners insurance company. Kore.ai Fraud Detection Insurance fraud costs each year today. AI, on the other hand, can assist insurance companies in detecting anomalies in claims data and identifying misleading information that clients use to obtain a lower premium or a larger claim payout. Machine learning algorithms can sift through previous data to see whether anything is out of the ordinary. Certain trends can be highlighted, alerting you to the possibility of fraud. It's unrealistic to anticipate such a high level of inspection from a human eye alone, but a human eye combined with AI can do wonders. more than $40 billion Efficient Underwriting When the data volume is enormous, AI data analysis outperforms human data analysis. Instead of manually scrutinizing data, the underwriter may focus on strategy and with AI solutions. Algorithms based on artificial intelligence can improve the efficiency of time-consuming underwriting operations. To come up with a strong underwriting plan, a combination of AIs is sometimes deployed. portfolio management Advanced Forecast Although it is impossible to foresee the market's future, thanks to developments in AI technology, many plausible predictions may still be made today. Many organizations operate in this manner as well, and the insurance industry has done an excellent job of harnessing the power of advanced forecasts. Artificial intelligence can help organizations spot trends in data that would otherwise go undetected, making it much easier for them to navigate the market than before. Furthermore, because of advancements in the discipline, the quality of those predictions has improved. Why isn't AI Fully Utilized in the Insurance Industry? This is due to certain doubts about its potential to improve the experience for both insurance firms and their clients. By no means is AI a perfect system; it has flaws that computer scientists are continuously attempting to resolve. These are only a few of the key implications of AI for the insurance sector. Job Loss Many insurance companies are considering AI as a means of lowering their staffing costs. When making a claim or dealing with other significant issues, however, many clients still prefer to speak with a live representative. Another concern is that if the insurance sector transitioned to an AI-based workforce, millions of jobs would be lost. Wellness Pricing AI can collect information on a person's habits. As a result, it's feasible that insurers could exploit such information to determine discriminatory premiums. For example, a health insurance company may use data from a fitness tracker to determine the pricing based on the person's projected health risks. Privacy Issues No doubt your personal information is likely to be stored on the cloud, regardless of whether or not companies use it. Many individuals, however, are wary of firms having greater access to their data (recall ), even if it is used to deliver more tailored experiences. WhatsApp’s controversial privacy policy Expected Trends in the Insurance Industry with Artificial Intelligence You'd undoubtedly agree that the insurance business could use a boost, which is just what AI aims to achieve. The technology could have a significant impact on both insurance companies and policyholders in the not-too-distant future. As AI makes inroads into the insurance sector, here are some of the patterns you can expect to see: Data Veracity: The goal of data veracity is to improve data accuracy so that it can be used to make better business decisions. This will aid insurance companies in the battle against fraud and the early detection of potential dangers. Extended Reality: (XR) is an umbrella name for immersive technologies that allow the virtual and physical worlds to merge. To put it another way, extended reality uses virtual and augmented reality to transport individuals to different locations. You might be able to go for lunch in a new country or shop for a new home anywhere in the world as if you were truly there. Insurance firms may use this technology to digitally check homes after a claim is filed or examine the safety features of a vehicle before providing a quote. Extended reality Intelligent Distribution System: Insurance companies will be able to modernize their IT infrastructure by implementing intelligent distribution systems. This will revolutionize online tools and mobile apps, as well as aid to develop more user-friendly website experiences. AI is the Future In the future, AI is poised to disrupt the insurance industry like never before, affecting both insurers and their customers. Customers may benefit from a more seamless user experience as well as lower rates. Insurance firms may be able to save money by streamlining their operations. The possibilities with AI are endless, and it will only be a matter of time before we witness these advancements.

Unlocking the Power of Artificial Intelligence in the Insurance Industry

While many Artificial Intelligence (AI) and Machine Learning conferences were paused in 2021, those looking to attend one in 2022 are in luck -- in-person conferences are back! However, many conferences will also continue to offer virtual options as well for those not wishing to venture back out quite yet or for those who like the ease of participating from their living room. AI conferences for 2022 range from the most technical to business-focused to academic. Many cover the full spectrum, with specific tracks and workshops designed for those looking to specialize. Topics will include general AI, strategy, privacy, Deep Learning, Machine Learning, data analytics, data science, big data, natural language processing (NLP) like , robotics, risk management, surveillance, and more. Speech-to-Text Here’s the full list of top AI conferences to attend in 2022: 1. Gartner Data & Analytics Summit The takes place in March in Orlando, Florida. Top leaders in data analytics and AI will gather to discuss business strategy, technology, and leadership in an attempt to discern how to deliver value in an ‘uncertain’ world. The conferences’ seven tracks ensure that there are sessions for all interests. Gartner Data & Analytics Summit : March 17-22, 2022 : Orlando, FL : $3,250+ : When Where Pricing Notable Speakers Pieter den Hamer, Sr. Director Analyst, Gartner Donald Fienberg, Distinguished VP Analyst, Gartner Gareth Hershel, VP Analyst, Gartner Sally Parker, Sr. Director Analyst, Gartner Register . here 2. World Artificial Intelligence Cannes Festival (WAICF) The , is a hybrid conference and exhibition with additional workshops and demonstrations taking place in February in Cannes, France. The conference aims to explore AI topics related to society, technology, strategy, , and applications. Attend workshops run by big names like Meta and IBM or listen to world class speakers in the numerous conference sessions. World Artificial Intelligence Cannes Festival, or WAICF API privacy : February 10-12, 2022 : Cannes, France and Digital : 440 Euros+ (Full Pass), also free tier : When Where Pricing Notable Speakers Darlington Akogo, Founder/Executive Director, Minohealth AI Labs Beena Ammanath, Executive Director, Global Deloitte AI Institute Fabian Aufrechter, Head, Havas Sovereign Technologies Elias Baltassis, Partner/Director, Data & Analytics, Boston Consulting Group Register . here 3. Re-work Deep Learning Summit The , sponsored by Re-Work, brings together industry experts and academic researchers to explore the latest advancements in the fields of Deep Learning and Artificial Intelligence. Though held in person in San Francisco, the conference also has a fully virtual option for those not wishing to travel. Deep Learning Summit : February 17–18, 2022 : San Francisco, CA (or virtual) : $795-$1,695 ($199 for virtual ticket) When Where Pricing Notable Speakers: Maithra Raghu, Sr. Research Scientist, Google Brain Lex Fridman, AI Researcher, MIT Richard Socher, Founder, you.com Ilya Echstein, ML/AI Researcher, Google Register . here 4. Data Innovations Summit The brings together experts in the fields of data management, advanced analytics, and AI. Sessions cater to both the technical and business minds alike in an attempt to explore innovation in the field of AI. If in-person attendance is out of the question, consider the virtual option. Data Innovations Summit in Dubai : March 7th, 2022 : Habtoor Grand Resort, Autograph Collection, Dubai, or virtual : 189-399 Euros : When Where Pricing Notable Speakers Henrik Gothberg, Interim Global Insight and Data Backbone Owner, Scania Financial Services and Founding SEO, Scania and Dairdux Abel Aboh, Data Management Lead, Bank of England Max Metral, Senior Analytics Manager F1 David Dadoun, CDO, BRP Register . here 5. Big Data & Analytics Summit Canada 2022 The will offer a hybrid in-person and virtual experience with exhibit booths, on-demand speaking sessions, virtual networking, and more. Attend to forge relationships and discuss innovation in the field of data analytics. 2022 Big Data & Analytics Summit : April 5-6, 2022 : Toronto, Canada : TBD : When Where Pricing Notable Speakers (Past) Dr. Satyam Priyadarshy, Chief Data Scientist, Halliburton Chapin Flynn, SVP, Senior Data Advisor Advait Bopardikar, Canadian ML Practice Lead, Google Nastaran Bisheban, Chief Technology Officer, KFC Register . here 6. AI in RegTech Summit Sponsored by Re-work, the offers three tracks with multiple stages, round table discussions, Q&As, 1:1 meetings, technical labs, practical workshops, and more. Topics to be covered include Deep Learning, , regulation, big data, data security, KYC initiatives, risk management, regulatory reporting, market surveillance, and more. AI in RegTech Summit Automated Speech Recognition (ASR) : April 21-22, 2022 : New York : $795+ ($149 for virtual) : When Where Pricing Notable Speakers Kishore Karra, Executive Director, Model Review Group, J.P. Morgan Glenn Fung, Chief Machine Learning Research and Innovation Scientist, American Insurance Family Ruchi Sharma, VP, Group Strategic Analytics, Deutsche Bank Tyler Mey, Director, Data Science and Artificial Intelligence, Mastercard Register . here 7. World Summit AI Americas In its fourth iteration, the combines the full AI ecosystem--startups, academics, investors, business leaders, and tech companies in an interactive conference format. The conference will feature more than 150 speakers via two content tracks over its two day span. World Summit AI Americas conference : May 4-5, 2022 : Montreal, Canada : $449 CAD+ ($199 student) : When Where Pricing Notable Speakers (Past) Yoshua Bengio, Professor of Computer Science and Operations Research, University of Montreal Professor Daphne Koller, Founder and CEO, Insitro, Co-Founder, Coursera Marc G Bellemare, Research Scientist, Google Brain Hassan Sawaf, Director, Artificial Intelligence, Amazon Web Services Register . here 8. The AI Summit London The will feature 500 hours of content, 300+ expert speakers, 100+ solutions providers, and 13 stages. Attendees can choose from tracks across AI finance, AI healthcare, CX, Marketing and eCommerce, NLP topics like , Quantum Computing, AI Manufacturing, Cyber Security, and Computer Vision. 2022 AI Summit in London Sentiment Analysis : June 15-16, 2022 : London, UK : TBD : TBD When Where Pricing Notable Speakers Register . here

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