Go Autonomous

goautonomous

Although vehicle sensors collect massive amounts of data, only 5% of it is currently being used for product development. Better infrastructure and data processing hold the keys to progress. The promise of fully continues to excite and inspire millions of people around the world. The amazing things that safe, reliable, self-driving vehicles can do for humanity--from providing newfound mobility to senior citizens to reducing traffic accidents--are closer to our reach than ever. But we still have a long way to go. autonomous vehicles Along with the fuel (gasoline, diesel or electricity) that powers automobiles, autonomous vehicles require a fuel of their own to “drive” safely and effectively: data. Although that data, already collected by millions of sensors on thousands of vehicles around the world, is readily available, it’s not being utilized to its full potential. Today, automotive companies only access 5% of their vehicle data, while the remaining 95% becomes costly to store and optimise without the context necessary to make use of it. Sensor power the algorithms that make all levels of possible. As automotive companies aggressively pursue data-driven product development, data handling (including the exploration, querying, curation and evaluation of data) is a common bottleneck on the road to progress. datasets autonomous driving These essential but hard-to-manage datasets bring a unique set of challenges for those hoping to make use of them: Unstructured and diverse formats Need for rich semantics in order to access them Huge sizes that require high-performance computing Strong need for data versioning Access and security issues Need for continuous failure-case driven data exploration With the proper data processing and , engineering users can overcome all of these challenges and vehicle data can fulfill its potential. By upgrading legacy technology for data access and analysis, OEMs and mobility tech companies can bump up data utilisation rates by up to 40% and generate additional ROI as data exploration, search, analysis, anomaly detection, and evaluation require less manual engineering work and yield better results. analytics software Data infrastructure and management are being neglected Currently, vehicle technology developers are focused on models and ground truth labeling. These same developers are neglecting infrastructure upgrades, leading many to use legacy technology for data management. Terabytes of unstructured, unprocessed vehicle data easily overwhelm these systems, causing them to malfunction. Raw data has no metadata and billions of frames, and technology developers are left to use tools ill-suited to the task of data management to organize data on their own. machine learning Highly-paid engineers search datasets manually on sluggish database systems, .  Furthermore, the lack of insights garnered from these vehicle datasets means machine learning and data science systems are unable to effectively build AI functions that rely on sensor data as an input. spending up to 75% of their time on raw data handling issues instead of training and validating models Purpose-built data infrastructure is the solution Infrastructure that is designed and built specifically to house raw sensor data and extract insights is the answer. This infrastructure should be as simple and easy to navigate as spreadsheets and databases that bring order and usefulness to data in other industries. For vehicle sensor data, infrastructure that uses a flexible and minimalist data model and a scalable method to produce semantics, along with fast queries and integrated endpoints to use and share data, is most effective. SQL These and other infrastructure elements improve data re-use and result in faster insights. Enabled by semantic automation, data can be ranked according to its importance based on context, content, criticality, and usability. This reduces redundancy and maximizes information density. Low-importance data is archived or deleted, while high-importance data is easily accessible for everyone. Furthermore, data insights can be provided almost immediately with overviews of the content and the redundancy. Users can augment retention recommendations with tailored rule-based constraints, e.g. unprotected left turns should always be kept. Better autonomous vehicles, sooner Advances in automated data infrastructure and processing liberate automotive technology developers from the constraints of legacy technology systems. Automated, semantic data management systems such as make data handling easier than ever, enabling automotive companies to save valuable engineering time, boosting productivity and increasing overall utilization. SiaSearch Put simply, more robust, effective infrastructure for unstructured sensor data will lead to higher ROI on research and development by freeing up engineers to do what they do best: building algorithms that will power the autonomous vehicles of the future. And with those engineers working more efficiently, the dreams of autonomous vehicles improving our lives are that much closer to reality. of the SiaSearch data management platform to see how we can help. Dealing with lots of raw sensor data? Request a demo Originally published by Clemens Viernickel on: https://www.siasearch.io/blog/raw-sensor-data-needs-infrastructure-to-be-useful/

Automotive Companies only Access 5% of their Vehicle Data

White label DSP platform gives advertisers a chance to open independent ad tech businesses, set their own conditions and influence the outcomes of media-buying directly, without third-party providers. When advertisers go autonomous and what white-label gives them, let’s take a look. If of budding startups die within the first year because they find no demand for their product on the market, with programmatic business, everything is slightly different. Programmatic advertising has a reputation of a very profitable business — in 2019, the growing share of algorithmic (programmatic) ad spend outweighed all other kinds of media-buying ( digital display ad spend). more than 42% 84% of total According to the eMarketer, in 2020, programmatic ad spend will generate , and by the end of 2021, ad spend is supposed to grow to an unpreceded $79.75. This means only one thing: programmatic continues to redraw the digital advertising ecosystem. $57 billion Source Many years ago, built their own programmatic platform as a way to save a good share of their media costs. Last year, about a complete shift to in-house programmatic that was supposed to make their media purchases crystal-clear and transparent. Netflix and Kellogg Buyer reported If you are a novice in programmatic advertising, chances are you think in-house programmatic is for big businesses like these. However, brands that have tried white-label DSP prove the opposite — this new approach helps to build your own programmatic platform in-house without substantial financial injections and risks. So, if you're interested in how to enter Ad Tech business, let's make a profound analysis of some core things: ●      What is a DSP? ●      What is a White Label Technology? ●      How it is different from White Lable DSP? ●      How to find out whether you need to step on independent grounds of programmatic? What is a DSP and why advertisers use it: 3 most important functions Demand-side platforms automate the process of online media buying and this is their most important function. Technically speaking, it is a software platform that features connections to SSPs, ad networks and ad exchanges and enables advertisers to purchase inventory programmatically, usually via RTB (real-time bidding auction) as the picture below illustrates. Source DSP doesn’t shoot blindly to whatever bid comes its way; it needs a definite set of rules to act upon and advertisers provide the platform with such directions in the user interface. Targeting (OS, gadget, age, gender, zip, channel and more), frequency, budget limitations, filters, etc., — all these criteria take part in the decision-making process. They enable delivering ad messages to the right audience at the right time and device. They also limit the number of impressions per user to prevent ad fatigue and budget overspending. Strengths of DSP solution that can’t be found in manual media buying ● As mentioned above, the platform uses the rules defined by the advertiser to automate the purchasing process. Responsible technologies: AI and ML. Buying activities automation. ● . With automatic auctioning, bid approval and ad serving, there’s no need to negotiate the placements, make credit card transactions for every placement or arrange email correspondence. As soon as the user with relevant cookies opens the page (app), DSP defines the potential worth of the bid and joins the auction. Cost-efficiency and speed ● . Negotiated placement leaves little maneuvering for advertisers to change something about their ad, even if it doesn’t bring a return on investment — the ad is paid for and it’s placed somewhere for a definitive period. DSP platform makes real-time analytics. If something isn’t working, there’s always a chance to change the campaign flow in real-time. Real-time optimization ● . With DSP, advertiser gathers all campaigns in one place – on one DSP dashboard. Unlike manual placement, the advertiser doesn’t have to attract sales reps or agencies. Everything can be done by one person (self-serve DSP) or a team of DSP specialists that assist managed-serve campaigns (sometimes via ATDs). Only one person to operate What is a white-label technology? White label concept is a type of partnership where one company produces goods, services or technology and another one purchases it and re-sells it under their own brand. This way, each partner can focus on their core competencies (production or sale) and thereby save substantial costs, essential for new business launch. What is a white-label DSP platform? White label DSP platform enables taking this very same DSP technology in house, which means enterprises can purchase prebuilt DSP software, customize it and label it as their own solution. With white label DSP, the purchaser of the platform can operate as: A white label DSP reseller: sell DSP services for brands on a commission basis A media-buyer: optimize their own media buying costs An agency: streamline work for clients or provide them with self-serve accounts Where white-label outruns self-development? Like any programmatic solution, the demand-side platform is a complex thing that requires building the software system and plugging it all in the vast programmatic infrastructure. RTB, ad server connection and partner integrations are not even making a fraction on the full list of must-dos. When building a DSP platform from scratch, business owners need to attract the full specter of professionals in many areas of expertise: ad tech professionals, software developers, project managers and q/a testers. Then, they have to wait until the product roadmap slowly turns into a robust, working and market-licensed platform. This can take months, or more (depending on your team). half a year Source White-label DSP solution is a pre-built, SaaS-based technology that is already fully hosted, maintained, built with RTB integrations and only requires minimal customization. This alternative business building approach removes all the burdens of building the platform from scratch: developing, designing, testing, licensing and hiring people to scale it up when the business expands. Costs. ‘Building your own DSP solution’ is a phrase that already sounds costly. However, if building a platform from scratch may take somewhere between (again, heavily depends on your team), the white-label model will make it happen for  $1 000 to $50 000 (pre-made technologies are known for their affordability). $40,000 and $250,000 Why do companies look for white label DSP platforms? White label DSP platforms are gaining momentum because more companies are striving to take their programmatic purchases in-house. By the end of 2019, and 65% of publishers in Europe had taken their programmatic purchasing in-house. 38% of advertisers, 66% of agencies Source Why are they doing this? Even though programmatic allows agencies to simplify and automate media-purchasing as much as possible, it still experiences challenges associated with insufficient transparency, also known as a ‘black box problem.’ Some DSPs don’t disclose terms of cooperation, so the agencies can’t deliver to customers relevant information concerning service commissions and margins that DSP charged them with. As a result, brands see only the total cost of services and are unable to distinguish what they are paying for. By selecting an in-house strategy, expect to increase operational control, reduce media costs (42%) and gain greater transparency on where campaigns run (33%). 50% of advertisers Do you need to set up white-label DSP? White label DSP solution enables a simple shift to in-house programmatic media-buying. While self-serve DSP may perfectly satisfy the needs of a small brand, in some cases, business needs may overgrow the basic abilities of DSP. The following examples can perfectly illustrate this point: . Exactly, for this reason, buyer brought programmatic in-house and saved approximately . Sure, if your regular media-spend is small, there’s no point to worry about DSP service commissions — normally they won’t exceed 30% per bid. Saving money $10M during the first 6 weeks For instance, if you spend $1000 per month, it will barely reach $300. However, if regular media spend of yours climbs up to e.g. ~$29,000, you will leave $8,700 on the table. What is a white-label DSP in terms of payment — it is a subscription-based platform that only makes the fee of the generated ad spend. So, if the ad spend is ~$29,000, with a normal 5% fee per month, you give out $1,450 instead of $8,700. Source Building your platform with a white label DSP solution enables you to set up a functioning platform in less than a month. Most importantly, it helps to overcome one fundamental business building risks: as the platform is already time-proved and finished, this is a guarantee that technology will be bug-free and robust enough to measure up with big and reputable programmatic platforms. Scaling and avoiding risks. For some companies, a white-label solution is a chance to join an RTB ecosystem effortlessly, and for others, it is also a chance to scale their platform anytime. The solution is based on the remote servers, so updates, upgrades and improvements will be happening on the background — there’s no need to terminate operation in order to scale capacities. Customer data protection and advertising fraud are the weak spots of the advertising ecosystem. Brands that accumulate and use for targeting at times fear that it will flow through a third-party supplier to competitors. Ensuring safety and consistency. first-party data The world has known enough examples of data misuse — is only the loudest of them. For this reason, advertisers strive to independently control user data and quality of traffic (applying scanners and monitoring statistics in real-time). Cambridge Analytica scandal In order to extend potential reach, advertisers are often pushed to work with several DSPs, which creates another roadblock to effective targeting — bidding against your own bids in different auctions. White label DSP, in this situation, is the best shield against competing against yourself as you track each activity of the system. Source Additional strengths a white label DSP solution may have: Ability to connect existing SSP partners via API and obtain their user data Ability to order on-demand features, integration and customization Ability to support the widest range of ad formats Ability to modify the system and integrate external modules via API Things you should know before building white-label DSP platform White label DSP platforms are solving a lot of problems that non-proprietary ad tech solutions can’t. Still, can we expect that the advertising industry will completely transition to in-house programmatic in the years to come? On the one hand, big players are massively heading in-house; on the other hand, the number of brands that open their own programmatic platforms and departments is relatively small and there’s nothing wrong about it. The reasoning of enormous budgets behind opening your own programmatic platform is nothing more than a common misconception. As was illustrated on the media-budgets example above, if a company doesn't spend a lot of money on media, buys inventory rarely or seasonally, there is no need to buy advertising platforms and run independent advertising businesses. Why would you buy a ship if you only sail once per year? 1) Your budget. Plus, there will always be players who follow the model of key competencies and who will not try to embrace innovative technologies or business models just for the sake of it. Before you adopt any technology, you should always ask yourself what problem it is supposed to solve; only then will it bring you profits and desired return on investment. Not every advertising agency or enterprise has sufficient experience in the programmatic business. Sure, white label DSP providers will build the platforms from the ground up for you, but the promotion, market positioning and operation as a whole will be entirely on you. Your platform is your business — no one dictates you or patronizes you on what payment model to use, what partners to befriend or where to put the label in the dashboard. 2) Where to get expertise. In order to effectively cope with operational tasks and optimize ad campaigns on behalf of their clients (the success of which depends on so many conditions), it is necessary to have programmatic professionals in staff, sometimes entire departments. For this reason, even with the growing popularity of in-house, many companies will still rely on agency experience. The others, meanwhile, select the strategy where the DSP operation process is fully or partially outsourced. The takeaway DSP is a complex technological platform that streamlines and effectively automates media-buying. However, construction and launch of it can be very expensive if you build it from scratch. In addition to costs, you need to invest in development, testing, and platform customization, building your own infrastructure can take the time that otherwise could be spent on conquering a new programmatic niche. White label concept is an amazing choice if you keep it in mind the idea to start you own business in ad tech and plan to create a branded DSP technology of your own. For many enterprises and agencies, the construction of a DSP is impossible from a financial standpoint. However, if you spend a lot of your budget on media-buying, then the decision to get your own programmatic solution may look pretty appealing. White label DSP solves the problems of enhanced fees, insufficient transparency and accountability of the media-buying. It cannot be ruled out that in ten years, technologies like white label will make the programmatic businesses a commonplace for every company. However, in making such an important decision, every entrepreneur must also give oneself answers to the questions: “What gaps am I covering?”, “Who will be in charge of operations?” and finally “Am I ready to run my own advertising business?” ( : ) Disclaimer The author is the CMO at SmartyAds

How Demand Side Platforms (DSP) Can Help Recalibrate Ad Businesses in 2020

History, Waves and Winters in AI

An exploration of human-machine trust (image by Daryl Campbell, CCO, ) Sensum In the ever-accelerating march of big data and AI, we are sharing increasingly detailed and personal data with our technology. Many of us already freely publish our thoughts, our current location, our app-usage stats… a raft of personal information, handed to companies in the hope that they will give us something useful or entertaining in return. Increasingly we are sharing our physiological data too, whether we’re aware of it directly or not, such as heart rate through fitness trackers, or facial expression through a tsunami of ‘puppy-dog’ filtered selfies. The promise in return is that our apps, software, vehicles, robots and so on will know how we feel, and they will use that empathy to provide us with better services. But as technology starts to get really personal, and gives it a ‘life’ of its own, we have to take a fresh look at what trust and ethics mean. autonomy OK, I admit, this topic is way too big for a brief overview. Interviewing experts for this story melted my head. Their ideas were simultaneously amazing and terrifying. My aim here is simply to highlight a few things you may wish to consider in earning the trust of your customers while also trying not to fuck up society and end the human race. Also, I’m a hopeless, reckless optimist. If I wasn’t I would have left the startup world years ago. So I will leave the doomsaying to other people. There is plenty of it about and plenty more to come, because bad news sells. Scary is sharey. And robots can be scary. Whenever an organisation convinces its users to share intimate information, such as their physiological state, their location, or imagery from their surrounding environment, there is an inherent contract of trust between the two parties. That contract is a fragile and valuable thing that is easy to lose or abuse, and very difficult to rebuild once it’s broken. In most cases, we probably don’t even think about it when we sign up for new services because we’re more concerned about what they can do us, than what they might do us. But our relationship with technology deserves serious thought as AI and connected devices become increasingly ubiquitous. for to It only takes one dickhead to ruin the party. The Trust-Risk Relationship One of the first things to consider when calculating how much trust you will need to establish with your users is . You could say there is an inverse relationship between the size of the risk and the level of trust you start out with. risk The greater the risk, the more trust you need to earn. Take the example of how much you would trust an autonomous car. In this case it is life-and-death. If the machine crashes, you won’t just be able to press ctrl-alt-delete and get on with your day. So we can say that the initial trust gap is huge. That said, what if it was an aeroplane rather than a car? Currently, people tend to trust driving more than flying, in spite of the statistical fallacy in doing so. Cars have sat for a long time in a sweet-spot of high desire and trust. We feel more familiar with cars. This might help autonomous vehicle developers gain customers but we can’t guarantee that the public mindset won’t change as we go autonomous. If one autonomous vehicle in a million causes a fatal accident it will be global news, while millions of people a year are already killed by human drivers with little mention in the press. So… . Be transparent about what your technology is taking from them and what it does with that information. Be open about the inherent risks to them. Educate your users , to understand and mitigate where the trust issues appear in the journey of interaction with your product or service. Prioritise user-experience design from Day One Origins of the Trust Bias In unfamiliar or high-risk scenarios, we are probably biased towards distrust in the first place. But in familiar social relationships we lean towards trust. We give the other actor the benefit-of-doubt on our first interaction. This might make evolutionary sense: while we need to be cautious to avoid a fatal mistake, we also benefit from efficiencies in developing relationships by going with the flow. But if you push your luck you will have a lot of work to do to overcome that betrayal. So… your service to appear amenable from the initial user interaction. Hold their hand through the first steps in the process. Design The more your service, the more work you need to do to get your users comfortable with it. novel . Don’t push your luck with your users’ trust, you could balls it up for the rest of us. Err towards open Trust as an Emergent Property of Empathy Us humans are consummate mind-readers. We are always trying to imagine the other person’s worldview, and considering how to respond to it. This empathic processing is what companies like ours ( ) are modelling in software and devices to enhance human interactions with them. Sensum In trying to guess another person’s perspective, we make snap judgements about the extent to which we can trust them. Our trust assumptions in any given situation or relationship are based on factors such as: : how has the other actor behaved in the past? Historical performance : are they part of my group? Are they a familiar type of character? Tribalism : what are they doing right now? Current activity If we know the other actor well, or know that they know us well, and understand the context of our situation, we are more likely to assume greater trust in the interaction. Seen this way, it can be argued that you need empathy before you can have trust. The ability to judge another entity and adapt that judgement to the context of the situation on the fly is a kind of emotional intelligence that has many advantages. Machines at this stage have only rudimentary capabilities in this space. They can appear uncaring by being too distant, or annoy us by ‘acting’ too empathic and involved without having the requisite understanding of the trust relationship (think: ). Microsoft’s Clippy This presents a paradoxical design challenge. On the one hand, trust can be nurtured by presenting the user with an interface that behaves consistently so they always know what to expect of it. However, a highly empathic system should be able to adjust its behaviour to suit the user’s situation, mood, character, etc., choosing when to be more or less intrusive. Machines could provide an option to set our preferences for how empathic or human we want them to be from one interaction to the next. But this is inefficient. They should already know how to behave — assessing our state-of-mind and the surrounding context, and acting appropriately. That’s what a good friend would do without a thought. As suggested by theories such as ‘ ’, the snap-judgement we make on first encountering another entity can set the tone for the lifetime of our relationship with it. After that, efforts to adjust that image may only have a minor effect. I attempted to do my own mind-reading by including a swear-word near the start of this story, judging Medium readers to be comfortable with that kind of thing. These are the kinds of tricks we employ all the time to win each other over. thin-slicing No matter how empathic we are able to make human-machine interaction, we have to consider how ‘human’ we want our machines to appear to us. Too closely imitating human behaviour could unveil a whole new for human-machine interaction. Uncanny Valley So… from the start. If you can’t provide an incredible service, don’t brand it in a way that implies you can (think: the from Twitter’s clunky early days, or Google’s strategy). Manage expectations Fail Whale perpetual beta between the user and your system by designing it to behave consistently. Grow familiarity and confidence Then, more varied, dynamic levels of empathic response. gradually introduce into the mix, not just human feelings or behaviours, when training systems to be empathic. Eg. location, weather conditions, time of day, etc. More on that in our . Add context information recent story on sensor fusion smoothgroover22) How human-like should our tech be for us to trust it? (Image courtesy: Flickr user, Teaching Machines to be More Human Consider the different relationships that we can apply empathy, trust, morality and other human social heuristics to: : human-to-human H2H : human-to-machine H2M : human-to-(everything) H2X : machine-to-machine M2M : machine-to-infrastructure (eg. street signs, buildings, etc.) M2I : machine-to-(everything) M2X Each member of the network in a given situation must consider not only their own psychological metrics, but also those of the rest of the group. And this could apply just as much to human interaction with machines as within the machine relationships with each other. Thus the application of traditionally ‘human’ heuristics, such as empathy, trust and morality, will extend to the M2M interactions, with which we may have little involvement. When the interactions are solely machine-to-machine, attributes like trust and empathy may be very different to what we’re used to. Electronic social interactions might be cold, algorithmic, inhuman processes. But then again, we have to ask ourselves how different that is to our own actions, when you view them from an evolutionary perspective. We feel ‘emotional’ responses to stimuli around us. Those emotions evolved to drive us to act in an appropriate manner (run from it, attack it, have sex with it, etc.). We say that we avoided a dark alley because we felt scared, or pursued a relationship because we fell in love. Our bodies tell us, ‘if happens then do ’. this that If x then y. Our emotions are Nature’s algorithms. So… what rules and interactions you can design from the start to make your systems behave socially. Explore your rules not just to human-machine interaction but to all possible relationships in the network. Apply Getting Along in our Empathic Future Building heuristics for social behaviour isn’t new. It has already been done in systems from communication networks to blockchains. And those algorithmic heuristics have evolved over time, adapting to new opportunities or increased complexity. Autonomous systems are learning by themselves, from their environment and experience, just like we learned over millions of years, but at blinding speed. Soon we may not even be able to understand the languages and actions of the machines that we originally created. Machines will get better at understanding and serving us as they spend more time learning about us. Therefore we can expect a transition period as we gradually hand over more control to autonomous systems such as vehicles and virtual assistants. Before our technological creations leave us behind in a runaway evolutionary blur, we must consider how to teach them empathy and morality. But machines have a fundamentally different baseline for concepts such as morality. The costs and benefits of actions such as killing another machine aren’t the same as they are between animals in nature. Social interactions are fuzzy, not only in their logic but also their application, so they may be very difficult to . Perhaps we could start by applying a ‘do unto machines as you would have them do unto you’ rule. engineer The age of empathic autonomy is commencing, and my guess is that we would all benefit from the same precept my colleagues and I wrote down as Rule Number 1 at : . Sensum don’t be a dick Special thanks to Dr Andrew Bolster (Lead Data Scientist at Sensum) and (School of Psychology, Queen’s University Belfast) not only for providing much of the thinking behind this story but also blowing my tiny little mind in the process. Dr Gary McKeown

How to Engineer Trust in the Age of Autonomy

Every AI Prediction That Came True in 2024—and the Ones That Didn't

Todas las predicciones de IA que se cumplieron en 2024 (y las que no)

Toutes les prédictions de l'IA qui se sont réalisées en 2024, et celles qui ne se sont pas réalisées

Todas as predicións da IA que se fixeron realidade en 2024 e as que non o fixeron

2024-ին իրականություն դարձած յուրաքանչյուր AI կանխատեսում, և նրանք, որոնք չեն իրականացել

כל תחזית AI שהתגשמה בשנת 2024 - ואלה שלא

2024 年に実現したすべての AI 予測と実現しなかった予測

ხელოვნური ინტელექტის ყველა პროგნოზი, რომელიც ახდა 2024 წელს და ის, რაც არ შესრულდა

រាល់ការទស្សន៍ទាយ AI ដែលបានក្លាយជាការពិតនៅឆ្នាំ 2024 ហើយអ្វីដែលមិនបានកើតឡើង

2024년에 실현된 모든 AI 예측과 실현되지 않은 예측

Wszystkie prognozy dotyczące sztucznej inteligencji, które spełniły się w 2024 r. — i te, które się nie spełniły

Todas as previsões de IA que se concretizaram em 2024 — e as que não se concretizaram

2024 දී සැබෑ වූ සෑම AI අනාවැකියක්ම - සහ නොකළ ඒවා

As Donald Trump prepares to take office, America’s artificial intelligence (AI) policy stands at a crucial turning point, sparking debates about the future of AI development and regulation. His victory as the 47th president has already earned congratulations from tech giants like Jeff Bezos and ignited discussions about potentially giving Elon Musk a significant role in shaping the country’s AI policies moving forward. Trump has vowed to reverse many of the AI executive orders put in place by the Biden administration, arguing that they "stifle AI innovation" and push "radical left-wing ideas" onto the development of the technology. Although it's unclear what the direct impact of this repeal will be, it signals a move toward deregulation. The rhetoric surrounding it is dividing opinions, turning AI policy into a partisan issue—although voters across party lines support balanced regulation. Industry experts, however, are concerned that cutting back regulation may jeopardize AI safety at a time when it’s needed most. “The U.S. must focus on a solid and efficient infrastructure that allows organizations evaluating AI systems and their deployment to publish credible and verifiable information about these systems, including their origins, training data, sensor data provenance, and any security incidents,” Dave Maher, CTO of Intertrust Technologies, told me. “These filters should enforce safeguards and test for violations of ethical principles, such as promoting or endorsing harmful behaviors like violence in an AI model’s outputs.” A New Direction: Rolling Back AI Regulation? Musk, who supported Trump throughout his campaign, has been appointed to lead the newly created Department of Government Efficiency (DOGE)—a role aimed at overhauling the nation’s regulatory framework. He’ll be working alongside entrepreneur and politician Vivek Ramaswamy in this ambitious effort. Meanwhile, Americans for Responsible Innovation (ARI), a nonprofit focused on promoting responsible AI development, has started a petition with a goal of 10,000 signatures, urging Trump to appoint Elon Musk as a special advisor on AI. The group argues that Musk’s deep technical expertise and outspoken commitment to AI safety make him the perfect person to lead the U.S. in this crucial tech race. “The U.S. should lead the world in advancing AI safely and securely. No one is better equipped to help the Trump Administration make America lead on AI than Elon Musk,” reads the petition page on ARI’s website. “With proper mechanisms in place to handle conflicts of interest, Musk would be an invaluable asset for helping the Trump administration navigate the development of this transformational technology. Help us tell President-Elect Trump to make Musk a Special Advisor to the President on AI.” Musk has long warned about AI’s existential risks and expressed concern over AI becoming too powerful too quickly. But critics aren’t convinced. Some remain skeptical about Musk’s role in shaping AI policy, pointing to his decision to distance himself from OpenAI, a company he helped create, as well as his outspoken opposition to AI regulations. “There’s no way for Elon Musk to be unbiased, just like there’s no such thing as a truly ‘unregulated market.’ The absence of regulation is, in itself, a form of regulation—one that gives big corporations free rein to push their own agendas,” Hamid Ekbia, Director of the Autonomous Systems Policy Institute (ASPI) and Professor at Syracuse University, told me. “When Musk talks about deregulating AI, what he’s after is the chance to outpace his competitors. That's why he and others tend to exaggerate the existential risks of AI.” China’s Growing AI Prowess Fuels U.S. Concerns The U.S. faces mounting competition from China, which has invested heavily in AI development intending to surpass America by 2030. The AI race has become a central issue in discussions on national security, with both Democrats and Republicans viewing AI technology as a crucial component in defense strategies. China’s lower labor costs and focus on model training give it an edge in the AI race. Trump’s administration will likely continue to tighten restrictions on Chinese access to advanced semiconductors, a strategy initiated during his first term and expanded under President Biden. Trump’s stance on AI has been erratic, often praising its potential while warning of its dangers. He said that the U.S. would require vast infrastructure upgrades for AI, particularly in energy and computing power, to ensure the U.S. maintains its lead over China. To secure America’s technological position, experts call for ambitious investments in AI infrastructure. A recent proposal from OpenAI suggests creating "National Transmission Highways" to modernize the power grid and meet AI’s immense energy demands, a plan that aligns with Trump’s vision of improving infrastructure. “AI can optimize energy distribution and usage through virtual power plants, which manage thousands of components involved in energy production, storage, and consumption,” added Maher. “However, this is only feasible with extensive automation and AI to support precise decision-making. AI safety and security will be crucial for such systems’ successful, large-scale deployment.” Future of AI Regulation: What’s at Stake? Despite widespread bipartisan support, the U.S. AI Safety Institute (AISI) —an organization created after Biden’s executive order to spearhead government efforts on AI safety—could face an uncertain future under President-elect Trump’s newly created DOGE. The department is expected to target federal programs for cuts, and AISI might be on the chopping block. However, tech leaders, lawmakers, and advocates are rallying for a more nuanced approach, arguing that safeguarding AI technology is critical to both national security and ethical progress. Moreover, AI companies like Leading OpenAI and Microsoft are vocal about the need for strong safeguards to maintain the U.S.’s position as a global leader in AI. “AI regulation has to start at the top,” Raj De Datta, CEO and co-founder of Bloomreach, shared with me. “A handful of companies dominate the AI market, and everyone else depends on their data centers or the models they produce. It’s vital that we start with these tech giants—ensuring they respect privacy, operate fairly, use diverse datasets, and uphold values we all agree on. That’s how we get the outcomes we want, the kind that benefit society.” But others, like Ekbia, caution that the current profit-driven approach of big tech companies is unlikely to prioritize the safety of their systems, let alone ethical or environmental concerns. He pointed to recent controversies at OpenAI as evidence that many tech companies put profit—their so-called "bottom line"—ahead of legal and ethical considerations. “How can we expect companies like Google, which has moved operations to tax havens like Ireland or the Cayman Islands, to act responsibly when it comes to developing AI?” he asks. The current situation underscores a key tension: balancing innovation with accountability. As Trump prepares to reshape U.S. AI policy, the industry faces a period of uncertainty. Whether his administration will accelerate or stifle innovation remains to be seen, but one thing is clear: the stakes for AI safety, security, and leadership are higher than ever before. As Donald Trump prepares to take office, America’s artificial intelligence (AI) policy stands at a crucial turning point, sparking debates about the future of AI development and regulation. His victory as the 47th president has already earned congratulations from tech giants like Jeff Bezos and ignited discussions about potentially giving Elon Musk a significant role in shaping the country’s AI policies moving forward. Trump has vowed to reverse many of the AI executive orders put in place by the Biden administration, arguing that they "stifle AI innovation" and push "radical left-wing ideas" onto the development of the technology. Although it's unclear what the direct impact of this repeal will be, it signals a move toward deregulation. As Donald Trump prepares to take office, America’s artificial intelligence (AI) policy stands at a crucial turning point, sparking debates about the future of AI development and regulation. His victory as the 47th president has already earned congratulations from tech giants like Jeff Bezos and ignited discussions about potentially giving Elon Musk a significant role in shaping the country’s AI policies moving forward. Trump has vowed to reverse many of the AI executive orders put in place by the Biden administration, arguing that they "stifle AI innovation" and push "radical left-wing ideas" onto the development of the technology. Although it's unclear what the direct impact of this repeal will be, it signals a move toward deregulation. AI executive orders The rhetoric surrounding it is dividing opinions, turning AI policy into a partisan issue—although voters across party lines support balanced regulation. Industry experts, however, are concerned that cutting back regulation may jeopardize AI safety at a time when it’s needed most. “The U.S. must focus on a solid and efficient infrastructure that allows organizations evaluating AI systems and their deployment to publish credible and verifiable information about these systems, including their origins, training data, sensor data provenance, and any security incidents,” Dave Maher , CTO of Intertrust Technologies, told me. “These filters should enforce safeguards and test for violations of ethical principles, such as promoting or endorsing harmful behaviors like violence in an AI model’s outputs.” Dave Maher Dave Maher A New Direction: Rolling Back AI Regulation? Musk, who supported Trump throughout his campaign, has been appointed to lead the newly created Department of Government Efficiency (DOGE) —a role aimed at overhauling the nation’s regulatory framework. He’ll be working alongside entrepreneur and politician Vivek Ramaswamy in this ambitious effort. Meanwhile, Americans for Responsible Innovation (ARI), a nonprofit focused on promoting responsible AI development, has started a petition with a goal of 10,000 signatures, urging Trump to appoint Elon Musk as a special advisor on AI. The group argues that Musk’s deep technical expertise and outspoken commitment to AI safety make him the perfect person to lead the U.S. in this crucial tech race. Department of Government Efficiency (DOGE) Department of Government Efficiency (DOGE) “The U.S. should lead the world in advancing AI safely and securely. No one is better equipped to help the Trump Administration make America lead on AI than Elon Musk,” reads the petition page on ARI’s website. “With proper mechanisms in place to handle conflicts of interest, Musk would be an invaluable asset for helping the Trump administration navigate the development of this transformational technology. Help us tell President-Elect Trump to make Musk a Special Advisor to the President on AI.” petition page petition page Musk has long warned about AI’s existential risks and expressed concern over AI becoming too powerful too quickly. But critics aren’t convinced. Some remain skeptical about Musk’s role in shaping AI policy, pointing to his decision to distance himself from OpenAI, a company he helped create, as well as his outspoken opposition to AI regulations. “There’s no way for Elon Musk to be unbiased, just like there’s no such thing as a truly ‘unregulated market.’ The absence of regulation is, in itself, a form of regulation—one that gives big corporations free rein to push their own agendas,” Hamid Ekbia , Director of the Autonomous Systems Policy Institute ( ASPI ) and Professor at Syracuse University, told me. “When Musk talks about deregulating AI, what he’s after is the chance to outpace his competitors. That's why he and others tend to exaggerate the existential risks of AI.” Hamid Ekbia Hamid Ekbia ASPI ASPI China’s Growing AI Prowess Fuels U.S. Concerns The U.S. faces mounting competition from China, which has invested heavily in AI development intending to surpass America by 2030. The AI race has become a central issue in discussions on national security, with both Democrats and Republicans viewing AI technology as a crucial component in defense strategies. China’s lower labor costs and focus on model training give it an edge in the AI race. Trump’s administration will likely continue to tighten restrictions on Chinese access to advanced semiconductors, a strategy initiated during his first term and expanded under President Biden. Trump’s stance on AI has been erratic, often praising its potential while warning of its dangers. He said that the U.S. would require vast infrastructure upgrades for AI, particularly in energy and computing power, to ensure the U.S. maintains its lead over China. To secure America’s technological position, experts call for ambitious investments in AI infrastructure. A recent proposal from OpenAI suggests creating "National Transmission Highways" to modernize the power grid and meet AI’s immense energy demands, a plan that aligns with Trump’s vision of improving infrastructure. “AI can optimize energy distribution and usage through virtual power plants, which manage thousands of components involved in energy production, storage, and consumption,” added Maher. “However, this is only feasible with extensive automation and AI to support precise decision-making. AI safety and security will be crucial for such systems’ successful, large-scale deployment.” Future of AI Regulation: What’s at Stake? Despite widespread bipartisan support, the U.S. AI Safety Institute (AISI) —an organization created after Biden’s executive order to spearhead government efforts on AI safety—could face an uncertain future under President-elect Trump’s newly created DOGE. The department is expected to target federal programs for cuts, and AISI might be on the chopping block. However, tech leaders, lawmakers, and advocates are rallying for a more nuanced approach, arguing that safeguarding AI technology is critical to both national security and ethical progress. Moreover, AI companies like Leading OpenAI and Microsoft are vocal about the need for strong safeguards to maintain the U.S.’s position as a global leader in AI. “AI regulation has to start at the top,” Raj De Datta, CEO and co-founder of Bloomreach, shared with me. “A handful of companies dominate the AI market, and everyone else depends on their data centers or the models they produce. It’s vital that we start with these tech giants—ensuring they respect privacy, operate fairly, use diverse datasets, and uphold values we all agree on. That’s how we get the outcomes we want, the kind that benefit society.” But others, like Ekbia, caution that the current profit-driven approach of big tech companies is unlikely to prioritize the safety of their systems, let alone ethical or environmental concerns. He pointed to recent controversies at OpenAI as evidence that many tech companies put profit—their so-called "bottom line"—ahead of legal and ethical considerations. “How can we expect companies like Google, which has moved operations to tax havens like Ireland or the Cayman Islands, to act responsibly when it comes to developing AI?” he asks. The current situation underscores a key tension: balancing innovation with accountability. As Trump prepares to reshape U.S. AI policy, the industry faces a period of uncertainty. Whether his administration will accelerate or stifle innovation remains to be seen, but one thing is clear: the stakes for AI safety, security, and leadership are higher than ever before.

Trump’s 2024 Victory Sparks AI Policy Debate: What’s Next for America’s Tech Future?

Trumpovo vítězství v roce 2024 podnítí politickou debatu o AI: Co bude dál pro americkou technologickou budoucnost?

La victoria de Trump en 2024 genera un debate sobre políticas de inteligencia artificial: ¿qué le espera al futuro tecnológico de Estados Unidos?

Trumpen 2024ko garaipenak AI politikaren eztabaida pizten du: zer da Amerikako etorkizun teknologikorako?

Viktwa 2024 Trump la te pwovoke deba sou politik AI: kisa kap vini pou avni teknoloji Amerik la?

La vittoria di Trump nel 2024 accende il dibattito sulla politica dell’intelligenza artificiale: quale sarà il futuro tecnologico degli Stati Uniti?

トランプ大統領の2024年勝利がAI政策論争を巻き起こす：アメリカのテクノロジーの未来はどうなるのか？

ტრამპის 2024 წლის გამარჯვებამ გამოიწვია AI პოლიტიკის დებატები: რა არის შემდეგი ამერიკის ტექნიკური მომავლისთვის?

Трамптың 2024 жылғы жеңісі AI саясатына қатысты пікірталас тудырды: Американың техникалық болашағын не күтіп тұр?

트럼프의 2024년 승리가 AI 정책 논쟁을 촉발: 미국의 기술 미래는 어떻게 될까?

ට්‍රම්ප්ගේ 2024 ජයග්‍රහණය AI ප්‍රතිපත්ති විවාදයට තුඩු දෙයි: ඇමරිකාවේ තාක්ෂණික අනාගතය සඳහා මීළඟට කුමක් ද?

Trump-nyň 2024-nji ýyldaky ýeňşi AI syýasaty barada jedel döredýär: Amerikanyň tehnologiýa geljegi üçin näme bolar?

Trump'ın 2024 Zaferi Yapay Zeka Politikası Tartışmasını Başlattı: Amerika'nın Teknoloji Geleceğinde Sırada Ne Var?

O.XYZ, the leading decentralized Super AI project, announces the launch of OSOL100, a first-of-its-kind AI index token designed to capture the cumulative value of Solana’s top 100 AI projects. This innovative token provides users with direct exposure to Solana’s AI infrastructure, agents, and meme tokens, all through one easily managed and fully transparent investment tool. OSOL100 simplifies investment strategies while enhancing portfolio diversification. It tracks and represents the performance of the top 100 AI-focused projects within Solana’s thriving ecosystem, offering accessibility to the most promising developments. Each OSOL100 token functions as a decentralized share of the fund, hosted on DAOS.fun, providing proportional exposure to its assets. Launched by O.XYZ, OSOL100, OSOLDOCS aligns with the company’s mission to create the world’s first Sovereign Super AI — an AI owned and governed by the community to benefit humanity. Powered by SuperMissO, the first AI CEO in development, OSOL100 embodies O.XYZ’s vision of an autonomous, community-led future. OBOT token holders gain exclusive access to OSOL100, enhancing the value and utility of their existing holdings. About O.XYZ O.XYZ aims to reshape artificial intelligence by developing systems independent of corporate control. It focuses on making AI technology accessible, transparent, and community-driven, ensuring superintelligence serves humanity's interests. O.XYZ's technical foundation centers on building an AI ecosystem designed to be shutdown-resistant and self-led. Their key initiatives include developing 'Sovereign Super intelligence,' creating decentralized infrastructure, and researching hyper-fast AI systems. The project operates under the O.Systems Foundation, led by Ahmad Shadid. Shadid, who previously founded IO.NET– a $3B Solana DePIN — brings his experience to O.XYZ's work on building an autonomous, community-led AI ecosystem. Contact VP Biz Dev Hassan Tariq O.XYZ hassan@o.xyz This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here O.XYZ, the leading decentralized Super AI project, announces the launch of OSOL100, a first-of-its-kind AI index token designed to capture the cumulative value of Solana’s top 100 AI projects. This innovative token provides users with direct exposure to Solana’s AI infrastructure, agents, and meme tokens, all through one easily managed and fully transparent investment tool. OSOL100 simplifies investment strategies while enhancing portfolio diversification. It tracks and represents the performance of the top 100 AI-focused projects within Solana’s thriving ecosystem, offering accessibility to the most promising developments. Each OSOL100 token functions as a decentralized share of the fund, hosted on DAOS.fun, providing proportional exposure to its assets. Launched by O.XYZ, OSOL100, OSOLDOCS aligns with the company’s mission to create the world’s first Sovereign Super AI — an AI owned and governed by the community to benefit humanity. OSOLDOCS OSOLDOCS Powered by SuperMissO, the first AI CEO in development, OSOL100 embodies O.XYZ’s vision of an autonomous, community-led future. OBOT token holders gain exclusive access to OSOL100, enhancing the value and utility of their existing holdings. About O.XYZ O.XYZ aims to reshape artificial intelligence by developing systems independent of corporate control. It focuses on making AI technology accessible, transparent, and community-driven, ensuring superintelligence serves humanity's interests. O.XYZ O.XYZ O.XYZ's technical foundation centers on building an AI ecosystem designed to be shutdown-resistant and self-led. Their key initiatives include developing 'Sovereign Super intelligence,' creating decentralized infrastructure, and researching hyper-fast AI systems. The project operates under the O.Systems Foundation, led by Ahmad Shadid. Shadid, who previously founded IO.NET– a $3B Solana DePIN — brings his experience to O.XYZ's work on building an autonomous, community-led AI ecosystem. Contact VP Biz Dev Hassan Tariq O.XYZ hassan@o.xyz This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

O.XYZ Launches OSOL100 AI Index – The S&P 100 Of Solana’s AI Ecosystem

O.XYZ OSOL100 AI ኢንዴክስን ይጀምራል - የS&P 100 የሶላና AI ምህዳር

O.XYZ lanza el índice de inteligencia artificial OSOL100: el S&P 100 del ecosistema de inteligencia artificial de Solana

O.XYZ lanza OSOL100 AI Index: o S&P 100 do ecosistema de IA de Solana

Az O.XYZ elindítja az OSOL100 AI Indexet – a Solana mesterséges intelligencia ökoszisztémájának S&P 100-át

O.XYZ משיקה את OSOL100 AI Index - S&P 100 של מערכת ה-AI של סולאנה

O.XYZ が OSOL100 AI インデックスを開始 – Solana の AI エコシステムの S&P 100

O.XYZ გამოუშვებს OSOL100 AI ინდექსს - სოლანას AI ეკოსისტემის S&P 100

O.XYZ ले OSOL100 AI इन्डेक्स सुरु गर्‍यो - सोलानाको AI इकोसिस्टमको S&P 100

O.XYZ د OSOL100 AI شاخص پیل کړ - د سولانا د AI اکوسیستم S&P 100

O.XYZ запускає OSOL100 AI Index – S&P 100 екосистеми AI Solana

O.XYZ نے OSOL100 AI انڈیکس کا آغاز کیا – سولانا کے AI ماحولیاتی نظام کا S&P 100

O.XYZ OSOL100 AI indeksini ishga tushirdi - Solana AI ekotizimining S&P 100

In a significant move, Binance Futures, the leading cryptocurrency derivatives exchange platform, today announced the launch of its CGPT/USDT Perpetual Contract. This latest addition expands the platform's trading offerings while highlighting the growing prominence of AI-focused cryptocurrencies in the digital asset ecosystem. The new perpetual contract introduces advanced trading capabilities for ChainGPT token (CGPT), offering traders leverage of up to 75x with USDT as the settlement asset. This addition to Binance Futures' suite of products demonstrates the platform's commitment to providing diverse trading opportunities while meeting the evolving demands of cryptocurrency traders. Commenting on this, Ilan Rakhmanov, Founder of ChainGPT and CEO of ChainGPT Software said, “The listing of CGPT on Binance Futures is a testament to the token’s utility and importance in the Web3 space. It offers our community another dynamic way to engage with CGPT while showcasing its role in the expanding ChainGPT ecosystem.” The perpetual contract format allows traders to hold positions indefinitely without expiration dates, providing flexibility for short-term and long-term trading strategies. Implementing a ±2.00% capped funding rate with four-hour settlements ensures a stable and predictable trading environment for all participants. Trading of CGPT/USDT Perpetual Contracts will be available through Binance Futures' advanced trading interface, which provides robust risk management tools and real-time market data to support informed trading decisions. Key Features of the CGPT/USDT Perpetual Contract: Maximum leverage: 75x
Settlement asset: USDT
Trading availability: 24/7
Capped funding rate: ±2.00% with four-hour settlement intervals
Multi-Assets Mode support: Enables trading using various margin assets, including BTC For more information about the CGPT/USDT Perpetual Contract and how to start trading, please visit Binance Futures' official website or contact the support team. About ChainGPT Incepted in 2023, ChainGPT is a leading provider of AI-powered tools for the blockchain and Web3 industries. It emerged as a project to bridge the gap between blockchain technology and AI, creating innovative solutions for the Web3 ecosystem. Leveraging advanced AI techniques, ChainGPT enhances blockchain functionality with its tools and applications, including SDKs and APIs for automated smart contract generation, a Web3 AI chatbot, an NFT generator, and an IDO launchpad. With established partnerships and collaborations with industry leaders such as Google, Nvidia, and BNB Chain, ChainGPT continues to pioneer efficient and user-friendly AI solutions in the blockchain space. As a relatively young but rapidly growing project, ChainGPT's mission is to revolutionize the intersection of blockchain and AI, with a vision to unlock the potential of autonomous AI agents in Web3. Users can learn more at: https://www.chaingpt.org/ General Resources: Website | Crypto AI Hub | ChainGPT Labs | ChainGPT Pad | CryptoGuard | Documentation | CGPT DAO | AI NFT Generator | Staking | Blog Community and Social Media: Twitter | Pad Twitter | Telegram | TelegramBot| Discord | Instagram | LinkedIn | YouTube | TikTok To learn more about ChainGPT, users visit the official ChainGPT.org website For all inquiries, users can contact support@chaingpt.org Contacts PR Lead Richa ChainGPT richa@chaingpt.org Sharon ChainGPT sharon@chaingpt.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here In a significant move, Binance Futures, the leading cryptocurrency derivatives exchange platform, today announced the launch of its CGPT/USDT Perpetual Contract. This latest addition expands the platform's trading offerings while highlighting the growing prominence of AI-focused cryptocurrencies in the digital asset ecosystem. The new perpetual contract introduces advanced trading capabilities for ChainGPT token (CGPT), offering traders leverage of up to 75x with USDT as the settlement asset. This addition to Binance Futures' suite of products demonstrates the platform's commitment to providing diverse trading opportunities while meeting the evolving demands of cryptocurrency traders. Commenting on this, Ilan Rakhmanov, Founder of ChainGPT and CEO of ChainGPT Software said, “The listing of CGPT on Binance Futures is a testament to the token’s utility and importance in the Web3 space. It offers our community another dynamic way to engage with CGPT while showcasing its role in the expanding ChainGPT ecosystem.” Commenting on this, Ilan Rakhmanov, Founder of ChainGPT and CEO of ChainGPT Software said, “The listing of CGPT on Binance Futures is a testament to the token’s utility and importance in the Web3 space. It offers our community another dynamic way to engage with CGPT while showcasing its role in the expanding ChainGPT ecosystem.” The perpetual contract format allows traders to hold positions indefinitely without expiration dates, providing flexibility for short-term and long-term trading strategies. Implementing a ±2.00% capped funding rate with four-hour settlements ensures a stable and predictable trading environment for all participants. Trading of CGPT/USDT Perpetual Contracts will be available through Binance Futures' advanced trading interface, which provides robust risk management tools and real-time market data to support informed trading decisions. Key Features of the CGPT/USDT Perpetual Contract: Maximum leverage: 75x Settlement asset: USDT Trading availability: 24/7 Capped funding rate: ±2.00% with four-hour settlement intervals Multi-Assets Mode support: Enables trading using various margin assets, including BTC Maximum leverage: 75x Settlement asset: USDT Trading availability: 24/7 Capped funding rate: ±2.00% with four-hour settlement intervals Multi-Assets Mode support: Enables trading using various margin assets, including BTC For more information about the CGPT/USDT Perpetual Contract and how to start trading, please visit Binance Futures' official website or contact the support team. About ChainGPT Incepted in 2023, ChainGPT is a leading provider of AI-powered tools for the blockchain and Web3 industries. It emerged as a project to bridge the gap between blockchain technology and AI, creating innovative solutions for the Web3 ecosystem. Leveraging advanced AI techniques, ChainGPT enhances blockchain functionality with its tools and applications, including SDKs and APIs for automated smart contract generation, a Web3 AI chatbot, an NFT generator, and an IDO launchpad. With established partnerships and collaborations with industry leaders such as Google, Nvidia, and BNB Chain, ChainGPT continues to pioneer efficient and user-friendly AI solutions in the blockchain space. As a relatively young but rapidly growing project, ChainGPT's mission is to revolutionize the intersection of blockchain and AI, with a vision to unlock the potential of autonomous AI agents in Web3. Users can learn more at: https://www.chaingpt.org/ https://www.chaingpt.org/ https://www.chaingpt.org/ General Resources: Website | Crypto AI Hub | ChainGPT Labs | ChainGPT Pad | CryptoGuard | Documentation | CGPT DAO | AI NFT Generator | Staking | Blog Website Website Crypto AI Hub Crypto AI Hub ChainGPT Labs ChainGPT Labs ChainGPT Pad ChainGPT Pad CryptoGuard CryptoGuard Documentation Documentation CGPT DAO CGPT DAO AI NFT Generator AI NFT Generator Staking Staking Blog Blog Community and Social Media: Twitter | Pad Twitter | Telegram | TelegramBot | Discord | Instagram | LinkedIn | YouTube | TikTok Twitter Twitter Pad Twitter Pad Twitter Telegram Telegram TelegramBot TelegramBot Discord Discord Instagram Instagram LinkedIn LinkedIn YouTube YouTube TikTok TikTok To learn more about ChainGPT, users visit the official ChainGPT.org website ChainGPT.org website ChainGPT.org website For all inquiries, users can contact support@chaingpt.org Contacts PR Lead Richa ChainGPT richa@chaingpt.org Sharon ChainGPT sharon@chaingpt.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Binance Futures Launches CGPT/USDT Perpetual Contracts

Binance Futures تطلق عقود CGPT/USDT الدائمة

Binance Futures lanza contratos perpetuos CGPT/USDT

Binance Futures lanse CGPT/USDT kontra tout tan

Binance Futures-ը գործարկում է CGPT/USDT հավերժական պայմանագրեր

Binance FuturesがCGPT/USDT無期限契約を開始

Binance Futures CGPT/USDT түбөлүк келишимдерин ишке киргизет

Binance Futures dia manomboka ny fifanarahana maharitra CGPT/USDT

Binance Futures e Thakgola Dikonteraka tša go se Fele tša CGPT/USDT

Binance Futures запускает бессрочные контракты CGPT/USDT

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