X2AI

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How Technology Can Help with Mental Health Diagnosis

Tell me, how does this sound: local weather updates, info on your flight, the latest news and much more all available in one place. In one place on your phone. Pretty cool right? Well, the coolness increases as there are no humans involved. Only you and a program that can understand and respond to certain phrases. These, my friend, are chatbots. In today’s world, the best way for a company to reach its consumer is through social platforms. According to an Oracle infographic, 90% of businessesuse Facebook to respond to service requests. As Deep Learning and NLP advanced so did the prospect of having a bot that would talk to the consumers. This same advancement has helped us apply automation in every place possible. So why not automate therapy as well? Mean number of mentally unhealthy days during past 30 days among adults aged ≥18 years Given the rise in mental illness in the United States and Worldwide and the lack of mental health infrastructure in some countries. We need to ask, Can chatbots provide a temporary (or permanent) form of therapy? Does it sound bizarre? Well maybe not. The Past In 1950’s Alan Turing published his now famous paper, ‘Computing Machinery and Intelligence’. To measure a machine’s intelligence, he designed the Turing test. If a machine can fool a human, in a conversation, to believe it to be human then it passes the test. In 1966, Joseph Weizenbaum, a professor at MIT Artificial Intelligence Laboratory, took interest in this. He made Eliza, the first chatbot. It was designed to fool people into believing that they were talking with a therapist rather than a bot. It worked by recognizing words from the input to reproduce a response. Then responded with certain keywords from a list of pre-programmed responses. Although with our current understanding of language we can build more complicated conversation interfaces, Eliza is still impressive. Other chatbots such as PARRY and JABBERWACKY have come along, but these weren’t made to simulate therapists. The Present However, we have come a long way from Eliza. Here are some of today’s therapy bots: , a New Zealand based company that made Nadia. Nadia is an animated human female that interacts with the user via a webcam. Actress Cate Blanchett has voiced Nadia and Dr. Mark Sagar has designed her. Her emotional intelligence, helps her look at the user’s face and connect with them on an emotional level. Although, she is a chatbot she is not a therapist. Nadia is there to support and help patients navigate the Australian NDIS(National Disability Insurance Agency). You can see her in action . But, the government has stalled her development due to certain risks related to high-tech projects in the past. Nadia: Soul Machines here Not a chatbot, but a worthy mention since this article is about mental health. Therachat is an application designed for psychotherapist and client communication. It has various features like assigning homework, emotion tracking, scheduling and direct messaging. Although, chatbots are on the rise Therachat’s ambition to enhance the therapist experience is also valuable. Therachat : Made by USC’s Institute for Creative Technologies, Ellie is a bot like Nadia. Funded by DARPA, she is part of a virtual reality program called SimSensei. She has a webcam and a sensor which she uses to read emotion and communicate. Her users are veterans with depression or PTSD (post-traumatic stress disorder). People are less open to revealing personal information to a human as compared to a bot, hence her purpose. And as this , it seems to work! But she warns her users that she isn’t a therapist but here to talk. Ellie: report reads : Built by Microsoft and hosted by Weibo, this chatbot is closer to a best-friend than a therapist. Microsoft built her by mining the Chinese internet for human conversations. She even uses emojis! Compared to Tay, whose racism got it suspended, Xiaoice has a much calmer personality. The Chinese user is open to talking to it about their breakups, family problems and anything else. It saves certain data from past conversations so it can bring them up in the future. The latter part has users doubting if they can trust it with their personal information. Given the amount of internet censorship in China, it is not surprising that she avoids sensitive political questions. Xiaoice An example conversation This isn’t a chatbot, but a startup that makes psychotherapeutic chatbots. They have made bots like Tess, “a psychological AI that administers highly personalized psychotherapy, psycho-education, and health-related reminders, on-demand”. Other include Emma, a Dutch-language bot designed to help people with mild anxiety. And Karim, a bot built to counsel Syrian refugees. This is clever. The cost of building a bot is far lower than sending a group of psychotherapists to the war zone. Also, since people have to open up to a bot they aren’t worried about social stigma. But, we can discuss it’s effectiveness for another time. X2AI : : I left the best for last. Woebot has a lot going for it apart from Andrew Ng coming as a part of its board of directors. You can read his announcement . Woebot isn’t a licensed therapist but he’s being used to target depression. The reason is the same: a growing mental disorder and stigma behind it. Woebot tries to understand what the user is saying and presents them with fun videos and good advice (try it!). Woebot here The Future It isn’t hard to notice the advantages of a therapeutic chatbot. When we don’t talk to a human, we are more likely to open up about our problems. These bots can never replace psychotherapists but they can improve the experience. As we saw with Therachat, helping the therapist in treating the patient is itself a big step. A mental health crisis and lack of infrastructure might make bots our saviors. We can have a world where everyone has their personal therapist. Like Xiaoice, they can talk to it without fear of being judged or ostracized. In extreme cases, the bot can contact family/spouse or send them to a proper psychotherapist. We shouldn’t hope for an all-around general therapist. Instead, what we might end up getting is a bunch of bots that help with different disorders. Take games for example. AI’s like DeepBlue and AlphaGo are far better at playing games than humans. But the same human that plays Chess can also play Go. As humans, we can combine all these things and do them and yet an AI cannot. Another example: a household bot. We dreamt of having a general purpose bot. A bot that could clean everything from clothes to dishes to the carpet. What we instead got was a Roomba, a dishwasher, and a washing machine. Perhaps that is where the future of psychotherapy lies. In different bots helping millions, open up about their issues and improving their lives.

The Chatbot will see you now or will it?

Hallucinations occur when an AI model provides a completely fabricated answer, believing it to be a true fact. The model is convinced that it has produced the correct answer (and is confident in doing so), yet the answer it provides is inherently nonsensical. We observed this behavior in ChatGPT, but it is a phenomenon that can actually occur with all AI models, where the model asserts a confident prediction that ultimately proves to be inaccurate. The most effective approach to addressing this issue is to comprehend our models and their decision-making processes, a concept that is covered by a field I am excited to discuss – explainable AI (XAI)! Watch the ... video https://youtu.be/6QVMCKmRJjQ?embedable=true&transcript=true Now that we have had an introduction to explainability in AI, we can dive deep into the topic of AI ethics and discuss why explainability is such a crucial aspect, provided by my friend Auxane Boch, an expert in the field, a research associate, and a . TUM IEAI freelancer At its core, explainability means that we understand how and why an AI system makes its decisions. This is important for several reasons. Firstly, without understanding the reasoning behind a decision, we can't blindly trust the system's output. This is particularly important in fields like healthcare or finance, where a wrong decision can have significant consequences. Secondly, without explainability, we can't identify and correct any biases or errors in decisions that may exist in the system. This is vital to ensure fairness, inclusivity, and the robustness of a system! However, there are some major challenges to achieving explainability in AI systems. When we talk about explainability, we can see it from two angles: technical transparency - usually targeted at engineers or technical experts, and understandability - for all types of users, from a tech company CEO to your baker. As you can imagine, understandability is a big challenge in itself! You cannot explain things in the same way to a highly digitally literate person as you would to your grandparents. Thus, one of the biggest challenges is the variation in digital literacy. Not everyone has the technical background to understand the complex algorithms and processes behind AI. This means that even if an explanation is provided, it may not be easily comprehensible to everyone. This could lead to a lack of trust in the system or even skepticism towards AI as a whole. In turn, this skepticism will impact the adoption of technologies and their acceptability. Another challenge is the diversity of cultures we have the chance to see in our world. Different cultures may have different expectations regarding explainability and ways to understand - just as we know counting can be different depending on the culture. For example, some cultures may value transparency and a clear understanding of decision-making processes through visualization and a lot of details, while others may prioritize accurate outcomes over explanations. This means that AI developers must be aware of cultural differences and adapt their systems according to their target population. In conclusion, explainability is a crucial aspect of AI ethics that enables us to trust and understand AI systems. However, achieving explainability is not without its challenges, including digital literacy and cultural differences. As we continue to develop and implement AI systems, we must strive to achieve an acceptable level of explainability while being aware of these challenges and working to overcome them. I hope you've enjoyed this ethics-related piece and the introduction to XAI! Auxane shares many such short pieces in if that is something you find interesting as well! my weekly newsletter

Tackling AI Hallucinations: The Importance of Explainability in AI (XAI)

An inmate at a New York correctional facility, Glenn Rodriguez, was due for parole soon. The man has been on his best behavior and was looking forward to being released and starting a new life. To Glenn’s horror, he was denied parole. An AI algorithm the parole board used gave this inmate a poor score, and as it wasn’t explainable, no one knew that something was going terribly wrong. Mr. Rodriguez fought his case and eventually was released after spending another unnecessary year in prison. Unfortunately, this type of mistake can occur whenever AI is deployed. If we don’t see the reasoning behind algorithms’ decisions, we can’t spot the problem. You can prevent this issue in your organization by following the explainable AI principles while . developing your artificial intelligence solution So, what is explainable artificial intelligence (XAI)? How to decide on the right level of explainability for your sector? And which challenges to expect on the way? What is explainable AI, and why should you care? When speaking of AI, many people think of black-box algorithms that take millions of input data points, work their magic, and deliver unexplainable results that users are supposed to trust. This kind of model is created directly from data, and not even its engineers can explain its outcome. Black-box models, such as neural networks, possess superior skills when it comes to challenging prediction tasks. They produce results of remarkable accuracy, but no one can understand how algorithms arrived at their predictions. In contrast, users can understand the rationale behind its decisions with explainable white box AI, making it increasingly popular in business settings. These models are not as technically impressive as black-box algorithms. Still, their transparency is a tradeoff as it offers a higher level of reliability and is preferable in highly regulated industries. What is explainable AI? Explainable AI (XAI) refers to a set of techniques, design principles, and processes that help developers/organizations add a layer of transparency to AI algorithms so that they can justify their predictions. XAI can describe AI models, their expected impact, and potential biases. Human experts can understand the resulting predictions and build trust and confidence in the results with this technology. When speaking of explainability, it all boils down to what you want to explain. There are two possibilities: Explaining AI model’s pedigree: how the model was trained, which data was used, which types of bias are possible, and how they can be mitigated. Explaining the overall model: this is also called “model interpretability.” There are two approaches to this technique: : a more understandable model, such as a decision tree, is used as an approximation of a more cumbersome AI model. Even though this technique gives a simple overview of what to expect, it remains an approximation and can differ from real-life results. Proxy modeling : designing AI models in a way that forces simple easy-to-explain behavior. This technique can result in less powerful models as it eliminates some complicated tools from the developer’s toolkit. Design for interpretability Source Explainable AI principles The US National Institute of Standards and Technology (NIST) developed : four explainable AI principles The system should be able to explain its output and provide supporting evidence (at least). There are several explanation types: ○ Explanations that benefit the end-user ○ Explanations that are designed to gain trust in the system ○ Explanations that are expected to meet regulatory requirements ○ Explanations that can help with algorithm development and maintenance ○ Explanations that benefit the model’s owner, such as movie recommendation engines The given explanation has to be meaningful, enabling users to complete their tasks. If there is a range of users with diverse skill sets, the system needs to provide several explanations catering to the available user groups This explanation needs to be clear and accurate, which is different from output accuracy The system has to operate within its designed knowledge limits to ensure a reasonable outcome Explainable AI example XAI can provide a detailed model-level explanation of why a particular decision was made. This explanation comes in a set of understandable rules. Following a simplified loan application example below, when applicants are denied a loan, they will receive a straightforward justification: everyone over 40 years old saves less than $433 per month and applies for credit with a payback period of over 38 years will be denied a loan. The same goes for younger applicants who save less than $657 per month. Why is explainable AI important? In some industries, an explanation is necessary for AI algorithms to be accepted. This can be either due to regulations and/or human factors. Think about brain tumor classification. No doctor will be comfortable preparing for a surgery solely based on “the algorithm said so.” And what about loan granting? Clients who got their application denied would want to understand why. Yes, there are more tolerant use cases where an explanation is not essential. For instance, predictive maintenance applications are not a matter of life or death, but even then, employees would feel more confident knowing why particular equipment might need preemptive repair. Senior management often understands the value of AI applications, but they also have their concerns. According to Gaurav Deshpande, VP of Marketing at TigerGraph, there is always a “but” in executives’ reasoning: “…but if you can’t explain how you arrived at the answer, I can’t use it. This is because of the risk of bias in the black box AI system that can lead to lawsuits and significant liability and risk to the company brand as well as the balance sheet.” The ideal XAI solution is the one that is reasonably accurate and can explain its results to practitioners, executives, and end-users. Incorporating explainable AI principles into intelligent software: Brings relief to system users. They understand the reasoning and can get behind the decisions being made. For example, a loan officer will be more comfortable informing a customer their loan application was declined if he can understand how the decision was made. Ensures compliance. By verifying the provided explanation, users can see whether the algorithm’s rules are sound and in accordance with the law and ethics. Allows for system optimization. When designers and developers see the explanation, they can spot what is going wrong and fix it. Eliminates bias. When users view the explanation, they can spot any presence of biased judgment, override the system’s decision, and correct the algorithm to avoid similar scenarios in the future. Empowers employees to act upon the system’s output. For example, an XAI might predict that a particular corporate customer will not renew their software license. The first reaction of the manager can be to offer a discount. But what if the reason behind leaving was poor customer service? The system will tell this in its explanation. Empowers people to take action. XAI enables the parties affected by certain decisions to challenge and potentially change the outcome (such as mortgage granting situations). Which industries need XAI the most? Healthcare Finances Automotive Manufacturing 1. Explainable AI in healthcare AI has many . Various AI-powered medical solutions can save doctors’ time on repetitive tasks, allowing them to primarily focus on patient-facing care. Additionally, algorithms are good at diagnosing various health conditions as they can be trained to spot minor details that escape the human eye. However, when doctors cannot explain the outcome, they are hesitant to use this technology and act on its recommendations. applications in healthcare One example comes from Duke University Hospital. A team of researchers installed a machine learning application called , which would send an alert when a patient was at risk of developing sepsis. The researchers discovered that doctors were skeptical of the algorithm and reluctant to act on its warnings because they did not understand it. Sepsis Watch This lack of trust is passed to patients who are hesitant to be examined by AI. Harvard Business Review published a study where participants were invited to take a free assessment of their stress level. registered for the test when they knew a human doctor would do the evaluation. Only 26% signed up when an algorithm was performing the diagnosis. 40% of the participants When it comes to diagnosing and treatments, the decisions made can be life-changing. No surprise that doctors are desperate for transparency. Luckily, with explainable AI, this becomes a reality. For example, Keith Collins, CIO of SAS, mentioned his company is already developing such a technology. Here is what he said: “We’re presently working on a case where physicians use AI analytics to help detect cancerous lesions more accurately. The technology acts as the physician’s ‘virtual assistant,’ and it explains how each variable in an MRI image, for example, contributes to the technology identifying suspicious areas as probable for cancer while other suspicious areas are not.” 2. XAI in finances Finance is another heavily regulated industry where decisions need to be explained. It is vital that AI-powered solutions are auditable; otherwise, they will struggle to enter the market. AI can help assign credit scores, assess insurance claims, and optimize investment portfolios, among other applications. However, if the algorithms provide biased output, it can result in reputational loss and even lawsuits. Not long ago, with its Apple Card product, which was inherently biased against women, lowering their credit limits. Apple’s co-founder, Steve Wozniak, confirmed this claim. He recalled that together with his wife, they have no separate bank accounts nor separate assets, and still when applying for Apple Card, his granted limit was ten times higher than his wife’s. As a result of this unfortunate event, the company was investigated by the New York State Department of Financial Services. Apple made headlines With explainable AI, one can avoid such scandalous situations by justifying the output. For example, loan granting is one use case that can benefit from XAI. The system would be able to justify its final recommendation and give clients a detailed explanation if their loan application was declined. This allows users to improve their credit profiles and reapply later. 3. Explainable AI in the automotive industry Autonomous vehicles operate on vast amounts of data, requiring AI to analyze and make sense of it all. However, the system’s decisions need to be transparent for drivers, technologists, authorities, and insurance companies in case of any incidents. Also, it is crucial to understand how vehicles will behave in case of an emergency. Here is how Paul Appleby, former CEO of a data management software company Kinetica, voiced his concern: “If a self-driving car finds itself in a position where an accident is inevitable, what measures should it take? Prioritize the protection of the driver and put pedestrians in grave danger? Avoid pedestrians while putting the passengers’ safety at risk?” These are tough questions to answer, and people would disagree on how to handle such situations. But it is important to set guidelines that the algorithm can follow in such cases. This will help passengers decide whether they are comfortable traveling in a car designed to make certain decisions. Additionally, after an incident, the provided explanation will help developers improve the algorithm in the future. 4. Explainable artificial intelligence in manufacturing AI has many applications in manufacturing, including predictive maintenance, inventory management, and logistics optimization. With its analytical capabilities, this technology can add to the “tribal knowledge” of human employees. But it is easier to adopt decisions when you understand the logic behind them. Heena Purohit, Senior Product Manager for IBM Watson IoT, explains how their AI-based maintenance product approaches explainable AI. The system offers human employees several options on how to repair a piece of equipment. Every option includes a percentage confidence interval. So, the user can still consult their “tribal knowledge” and expertise when making a choice. Also, each recommendation can project the knowledge graph output together with the input used in the training phase. Challenges on the way to explainable AI The need to compromise on the predictive power Black box algorithms, such as neural networks, have high predictive power but offer no output justification. As a result, users need to blindly trust the system, which can be challenging in certain circumstances. White box AI offers the much-needed explainability, but its algorithms need to remain simple, compromising on predictive power. For example, where algorithms produce remarkable results classifying brain tumors and spotting breast cancer faster than humans. However, when doctors decide on patients’ treatment, it can be a life and death situation, and they want to understand why the algorithm came up with this diagnosis. It can be daunting for doctors to rely on something they do not understand. AI has applications in radiology The concept of explainability There is no universal definition of explainability. It is often a subjective concept. Users might expect one type of explanation, while developers are willing to provide something else. Also, different audiences require tailored justifications, which results in one XAI system having to explain the same output in several different ways. Security and robustness-related issues With XAI, if clients gain access to the algorithm’s decision-making process, they might apply adversarial behaviors, meaning they will take deliberate actions to change their behavior to influence the output. One published a concern that someone with technical skills can recover parts of the dataset used for algorithm training after seeing the explanation, thereby violating privacy regulations. study How to get started with responsible and explainable AI When your company is preparing to deploy responsible XAI solutions, the first thing would be to determine what exactly you need to explain and to whom. Some of the questions addressed during the planning stage can be: Is it a particular decision or the overall model? Who is your audience? Are they technical users? Do you need to provide different explanations for every output, or does one explanation suffice for everyone involved? Next, your company needs to decide on the degree of explainability. Not all AI-based tools require the same degree of interpretability. six application criticality components that will help you determine your desired XAI level: PwC identifies Decision impact: how will the decision affect all the involved parties? This is not limited to revenue-related aspects. For example, an incorrect cancer diagnosis will have a dire impact, while recommending an irrelevant movie will not be a big issue. Control level: is it an autonomous AI system that makes decisions and acts upon them, or does the system’s output recommend actions to humans who can choose whether to follow or not? Risks: any potential harm that the algorithms can cause, such as impacting operations, sales, employees, any ethical, legal, environmental, or health-related considerations. Regulations: the legal framework of the industry and whether the decisions need to be explained. Reputation: how the system interacts with stakeholders — within the business and with the society in general. Rigor: if the application is accurate and able to generalize its conclusions to unseen data. Finally, even after explainable artificial intelligence is in place, it is best to take action to ensure your data usage is ethical. Perform timely audits of your algorithms. A strong explainability feature can reveal any bias sneaking into your software. However, if you settled on a limited explainability solution, bias could remain unnoticed. Also, you can subscribe to the . Or even develop your own set of ethical principles of data usage, as Microsoft did. They included fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. Partnership on AI consortium On a final note… Implementing traditional , let alone its explainable and responsible version. Despite the obstacles, it will bring relief to your employees, who will be more motivated to act upon the system’s recommendations when they understand the rationale behind it. Moreover, XAI will help you comply with your industry’s regulations and ethical considerations. AI is full of challenges If you have an idea of an explainable AI solution to build, or if you are still unsure of how explainable your software needs to be, . consult ITRex XAI experts

Explainable AI Principles: What Should You Know About XAI

Remote work and work from home are here to stay, . Now that more companies are doubling down on long-term remote, infrastructure, tools and resources are priority. that much is evident What are some of the biggest bottlenecks of virtual corporate culture? Meetings. In 2020, x.ai recorded From Zooms to Meets to phone calls, virtual meetings are growing by the sheer fact that more people are virtual. 182.4% year-over-year growth in the number of meetings scheduled. We can expect to see many advancements in work from home tools and and resources, as seen by the most recent partnership between Newton (previously CloudMagic) and x.ai. The Newton-x.ai partnership allows for seamless calendar scheduling for individuals and teams within the Newton platform. After a user links their x.ai account to Newton, they can share their availability instantly with easy keyboard shortcuts or  even have an AI Scheduler send available times directly to guests, right on the email thread. "The opportunity to pair x.ai scheduling with a user-first email platform was a no brainer. We've worked closely with the Newton team as our first scheduling API partner on what we believe is a simple-to-use and sophisticated way to schedule meetings natively from your inbox," said Dennis R. Mortensen, CEO and founder of x.ai. Founded in 2014, x.ai has spent more than 5 years tuning an AI-powered scheduling engine that instantly generates availability for individuals and teams based on their Meeting Templates. In 2020, x.ai recorded 182.4% year-over-year growth in the number of meetings scheduled on its platform, as scheduling software became a must-have for individuals, small teams, and enterprise companies. Newton itself has a great back story and was acquired by customers and fans Maitrik Kataria, of and Justin Mitchel of , in 2020 after its parent company went bankrupt. Originally one of the first email apps to offer snoozed emails and app integrations they’ve since added new features, fixed long overdue bugs and positioned the once legacy email client as a real contender in the space. Simform SoFriendly.com “It was a surreal moment to go from superuser to owner of my favorite email client. The new normal of work from home has propelled Newton further and faster than I could have hoped -- and has offered everyone the opportunity to become proficient schedulers. We love creating new features that help people get time back and x.ai gave us a great opportunity to do more,” said Maitrik Kataria, co-founder, Newton.

Newton partners with x.ai, now the first native calendar scheduling integration

By: Amanda Mulay Every day, in my role as senior talent manager at , I talk with our founders to learn about their hiring needs and challenges. During these conversations I get to know what they need to grow their teams, learn the ins and outs of the company’s structure, and hear about their stars and emerging leaders. In this new series, “What’s in a role,” I’ll interview individuals holding unique positions across , responsible for propelling crucial initiatives core to the function of the business. Lerer Hippeau our portfolio Kicking off this series is , VP of AI Training at x.ai. From how she got started in AI to her perspective on the future of automation, here’s a look at her day-to-day at , best known as the creator of AI scheduling assistants Amy and Andrew Ingram. Liying Wang x.ai Where did you start your career and how did you end up in your current role at X.ai? Amanda Mulay: I grew up in Palo Alto so tech is part of my constitution. I studied Existentialism and Literary Theory at Cornell, both of which had a profound impact on the way I view the world and have given me the tools to approach this linguistics problem that is one of the biggest challenges within my role. I think having a humanities background has been invaluable to my success in my job today. Liying Wang: I joined Lyft in the early days way back when it was still Zimride, and have been working in tech ever since. The possibilities to change the world in a positive and disruptive way are unlike any other industry. After Lyft, I tried my hand at my own startup, an e-commerce business for sports nutrition, which was an amazing growing experience. Then I joined Facebook in Menlo Park, where I ran the human annotator side of various machine learning products. The breadth of types of products and brilliant product and engineering partners gave me the opportunity to learn how to evaluate, optimize and scale data annotation. I heard about the role at New York-based x.ai through a Crunchbase notification. The company had just raised a Series B and I thought it was a super interesting opportunity and knew I wanted to stay in the AI/machine learning industry. I reached out cold and heard from Dennis [Mortensen] within a day. The rest is history! What does your role entail and how do you typically explain it? Mulay: My job is to solve complex problems in unchartered territory, and I wouldn’t have it any other way! My domain expertise is in building, running, and optimizing our AI Training team. The data annotations produced by this team provide the inputs for our machines to learn how to understand natural language in the production environment. Wang: I designed the infrastructure used by the AI Trainers to annotate data, created the annotation quality controls, processes, feedback mechanisms, and guidelines to teach the AI Trainers how to achieve high quality data annotations. Natural language is fluid and constantly evolving, and all of these components require perpetual flexibility and optimization. At a high level, what happens is this: if the machine has low confidence in its parsing of an email that Amy or Andrew receives, our system sends the AI Trainer team a data annotation task. This data annotation task will then feed back into the machine’s learning so it can handle it with higher confidence in the future. This process happens for specific micro tasks, so humans don’t ever decide how Amy will respond to a given email or get involved in the actual scheduling in any way. The data produced by the AI Training team must align with the machine learning models in addition to the product’s functional requirements. The inextricably linked systems require constant collaboration and fine tuning in order to work in harmony. The way I sometimes explain it to people is to imagine you have a baby robot, and you have to teach the baby robot to perform independent tasks on its own. In order to do that, you need to provide it loads of consistent inputs so it can learn, while accounting for other variables. What are the most common areas of confusion when you’re describing your role in AI? Mulay: I see AI as being able to create opportunity for mankind rather than take opportunity away. Unfortunately the latter is something I hear more frequently than the former. I see this occurring in two general ways. Wang: 1.) Being able to take over a vast variety of tasks which allows people to focus on more challenging, creative ones that AI does not have the cognitive capability of performing. Providing people time to channel their effort and energy towards more intricate and complicated problem-solving is highly beneficial. I certainly see x.ai as providing people increased efficiency, so instead of having to deal with the mundane task of going back and forth to schedule meetings, they can redirect their attention and focus on more important things. 2.) Disrupting industries for the betterment of humanity. AI can (and is starting to) make leaps and bounds towards revolutionizing how we approach other areas such as healthcare or transportation. While most of the misperceptions I hear are about AI itself, in terms of my role, most people are surprised I don’t have an engineering or programming background. The area around AI that is relatively less known is the training part which for me dealing with natural language processing, is basically a linguistics challenge. That’s why my background in Literary Theory, Deconstruction, and Operations align well. What do you find the most challenging about your role or responsibilities? Mulay: What drives me is being able to capture the limitless aspects of language with all of its subtleties/variations/unique eccentricities to fulfill data science and product requirements while achieving a level of quality so high we are striving to hit the ceiling of human performance. Wang: How do you see your role and professional skills growing from here? Mulay: AI is such a dynamic and growing field, so the opportunities are really endless. Whether that’s teaching Amy to learn different types of tasks, adding new features, expanding to other platforms to interact with Amy, or training Amy to understand other languages — all of these opportunities will require me to expand my current role and adjust for the nuances required for each new linguistic challenge. Wang: AI is going to keep growing and changing. It’s an industry that’s rapidly expanding right now and there’s AI everywhere we look. I think about AI as training machines by providing inputs that help teach them to perform tasks on their own. Every industry is trying to become more efficient, so the need for AI is omnipresent. How is this role unique to x.ai? Mulay: The data I’m training is specific to Amy, but the approach, philosophy and tenets are certainly transposable to different needs and products. Wang: What makes you most excited about working this area? Mulay: I love working within a space that is still relatively new. The uncertainty is invigorating, and I love tackling these big hairy problems and having to problem solve and iterate as we go. Wang: Interested in a role at x.ai? Check out current job openings here . Keep up with our community. Subscribe to the and follow us on and . Daily Roundup Twitter Instagram

What’s in a role: Liying Wang, VP of AI Training Operations at x.ai

Winter(Physics) is Coming It now looks like Large Language Models running on the GPT technology may be nearing a plateau in their development. This is from popular Physicist Dr. Sabine Hossenfelder’s latest YouTube video. https://www.youtube.com/watch?v=AqwSZEQkknU&embedable=true Dr. Sabine cites a couple of sources, including Bill Gates, who said last year that GPT 5 won’t be much better than GPT 4. And Prof. Gary Marcus says he has been drumming this particular beat for years. This goes against all the Ray Kurzweilian hype by Sam Altman and Ilya Sutskever as seen in the video. But if merely beautiful mathematics won’t spur the progress of Physics, as Dr. Sabine always reminds us, then merely wishful group-thinking won’t give us a superhuman artificial general intelligence (AGI). Why isn’t throwing as much data, computing power, and human supervision as can be afforded, going to birth us a superhuman AGI? Dr. Sabine has a ready answer. The problem, she explains, is the Decoupling of Scales. The Decoupling of Scales The world is broken up into scales. In the physical world, there is the scale of land animals like us humans. Then below that we have microbes, then molecules, atoms, and subatomic particles. In the opposite direction, we have aircraft and skyscrapers, mountains, planets, stars, solar systems, galaxies, etc. In the world of data, we have high-resolution data e.g. categories like “objects that can be touched”, “animals”, “cats”. Then low-resolution data to further expand the above categories e.g. “cat species”, “cat patterns”, “cat behavior at 8:00 am on Monday,” etc. There are other taxonomies, however. Some data is online and some is offline in books, papers, and people’s minds. We may have all the knowledge we desire at the high-resolution scale of human language creation on the internet. But that doesn’t equate to the much bigger, low-resolution data source that is human language creation in the world. Thus, Sam Altman’s AI is like Aristotle in the image below. Hoping to discover the Standard Model of Physics by looking at his hand. But without something like the Large Hadron Collider to poke subatomic particles and see the strange new data hiding within, Aristotle is at a loss on how to proceed. A Large Hadron Collider for AI GPT architecture, unveiled to the world only 2 years ago with the phenomenal ChatGPT, is likely nearing the end of its “improvement-ness”. Perhaps it is time to seriously consider passing the baton, and the VC funding, to something else. I am rooting for thermodynamic computing, currently being explored by companies like Extropic, which works by leveraging thermal noise to create random samples of data as in Stable Diffusion technology, then denoising using statistical analysis. In fact, in any neural network, the initial weights are usually initialized with random noise. During training, backpropagation shapes the final weights from this random noise - writes Laszlo Fazekas. The advantage lies in the chips operating at scales smaller than those of traditional digital computing, where quantum effects result in abundant thermal noise. Thermal noise has always been plenty, anyway. My kitchen stove is full of thermal noise. The real problem has always been how to understand it better through statistical chunking of its data, without throwing away data during regression analysis. My guess? Some sort of massive cloud computational AI training leveraging smartphones, bitcoin nodes, TV sets, radios, name it. Currently, allowing the AI some “thinking time'“ so that it runs longer inferences on the same data, is how AI companies are squeezing out some more juice. For example with ChatGPT’s o1 model. But maybe even training time should be extended. Maybe to get great AI, like a child, it needs decades of training from data collected from all over. Collected and slowly siphoned into a condensed AI model in a few servers. Sam Altman might need those trillions of government dollars for such a plan. Alas, they will likely not be easy to get. The D.O.G.E might not bankroll him (if xAI is suffering the same troubles). Meanwhile, Bitcoin is still growing As the above graphic shows, very few people are using Bitcoin. Yet it is already at $90k. Data from Bitbo shows that in fact, only 400,000 people are using Bitcoin daily. And only 106 million own Bitcoin. Contrast this with the 314 million people who have used AI tools as per data from Statista. ChatGPT alone has millions of users per day. Probably more than the entire Bitcoin blockchain combined! What’s more? AI amassed the bulk of these users in less than a year. This feels wrong. Bitcoin took years to grow. People sweated, lost, DCA’d, had faith. Maybe that’s it. ChatGPT has grown too fast. Now it has slowed down and everybody is sour. Sorry, ya’ll, there is still work to do. AGI will not be so easy to grasp, and GPT scientists should learn to hodl like Bitcoiners. Winter will come and go, but right now, it is time to pay the piper. Winter(Physics) is Coming It now looks like Large Language Models running on the GPT technology may be nearing a plateau in their development. This is from popular Physicist Dr. Sabine Hossenfelder’s latest YouTube video. https://www.youtube.com/watch?v=AqwSZEQkknU&embedable=true https://www.youtube.com/watch?v=AqwSZEQkknU&embedable=true Dr. Sabine cites a couple of sources, including Bill Gates, who said last year that GPT 5 won’t be much better than GPT 4. And Prof. Gary Marcus says he has been drumming this particular beat for years. This goes against all the Ray Kurzweilian hype by Sam Altman and Ilya Sutskever as seen in the video. said last year says But if merely beautiful mathematics won’t spur the progress of Physics, as Dr. Sabine always reminds us, then merely wishful group-thinking won’t give us a superhuman artificial general intelligence (AGI). Why isn’t throwing as much data, computing power, and human supervision as can be afforded, going to birth us a superhuman AGI? Dr. Sabine has a ready answer. The problem, she explains, is the Decoupling of Scales . Decoupling of Scales The Decoupling of Scales The world is broken up into scales. In the physical world, there is the scale of land animals like us humans. Then below that we have microbes, then molecules, atoms, and subatomic particles. In the opposite direction, we have aircraft and skyscrapers, mountains, planets, stars, solar systems, galaxies, etc. In the world of data, we have high-resolution data e.g. categories like “objects that can be touched”, “animals”, “cats”. Then low-resolution data to further expand the above categories e.g. “cat species”, “cat patterns”, “cat behavior at 8:00 am on Monday,” etc. There are other taxonomies, however. Some data is online and some is offline in books, papers, and people’s minds. We may have all the knowledge we desire at the high-resolution scale of human language creation on the internet . But that doesn’t equate to the much bigger, low-resolution data source that is human language creation in the world . human language creation on the internet human language creation in the world Thus, Sam Altman’s AI is like Aristotle in the image below. Hoping to discover the Standard Model of Physics by looking at his hand. But without something like the Large Hadron Collider to poke subatomic particles and see the strange new data hiding within, Aristotle is at a loss on how to proceed. A Large Hadron Collider for AI GPT architecture, unveiled to the world only 2 years ago with the phenomenal ChatGPT, is likely nearing the end of its “improvement-ness”. Perhaps it is time to seriously consider passing the baton, and the VC funding, to something else. I am rooting for thermodynamic computing , currently being explored by companies like Extropic, which works by leveraging thermal noise to create random samples of data as in Stable Diffusion technology, then denoising using statistical analysis. thermodynamic computing In fact, in any neural network, the initial weights are usually initialized with random noise. During training, backpropagation shapes the final weights from this random noise - writes Laszlo Fazekas. in any neural network , the initial weights are usually initialized with random noise. During training, backpropagation shapes the final weights from this random noise - neural network backpropagation The advantage lies in the chips operating at scales smaller than those of traditional digital computing, where quantum effects result in abundant thermal noise. Thermal noise has always been plenty, anyway. My kitchen stove is full of thermal noise. The real problem has always been how to understand it better through statistical chunking of its data, without throwing away data during regression analysis. My guess? Some sort of massive cloud computational AI training leveraging smartphones, bitcoin nodes, TV sets, radios, name it. Currently, allowing the AI some “thinking time'“ so that it runs longer inferences on the same data, is how AI companies are squeezing out some more juice. For example with ChatGPT’s o1 model. But maybe even training time should be extended. Maybe to get great AI, like a child, it needs decades of training from data collected from all over. Collected and slowly siphoned into a condensed AI model in a few servers. Sam Altman might need those trillions of government dollars for such a plan. Alas, they will likely not be easy to get. The D.O.G.E might not bankroll him (if xAI is suffering the same troubles). Meanwhile, Bitcoin is still growing As the above graphic shows, very few people are using Bitcoin. Yet it is already at $90k . already at $90k Data from Bitbo shows that in fact, only 400,000 people are using Bitcoin daily. And only 106 million own Bitcoin. Bitbo Contrast this with the 314 million people who have used AI tools as per data from Statista . ChatGPT alone has millions of users per day . Probably more than the entire Bitcoin blockchain combined! data from Statista has millions of users per day What’s more? AI amassed the bulk of these users in less than a year. This feels wrong. Bitcoin took years to grow. People sweated, lost, DCA’d, had faith. Maybe that’s it. ChatGPT has grown too fast. Now it has slowed down and everybody is sour. Sorry, ya’ll, there is still work to do. AGI will not be so easy to grasp, and GPT scientists should learn to hodl like Bitcoiners. Winter will come and go, but right now, it is time to pay the piper.

The AI Winter is Near, Courtesy of Physics

Der KI-Winter steht vor der Tür, mit freundlicher Genehmigung der Physik

El invierno de la IA se acerca, cortesía de la física

به لطف فیزیک، زمستان هوش مصنوعی نزدیک است

Tekoälytalvi on lähellä, fysiikan luvalla

एआई शीतकाल निकट है, भौतिकी के सौजन्य से

物理学のおかげでAIの冬が近づいている

AI қысы жақын, физиканың рұқсатымен

Зима искусственного интеллекта уже близко. Предоставлено Physics

Iyo AI Chando Yava Pedyo, Nekuda kweFizikisi

Dimri i AI është afër, me mirësjellje të fizikës

AI குளிர்காலம் அருகில் உள்ளது, இயற்பியல் உபயம்

AI qishi yaqin, fizika izni bilan

Hey Hackers, Forescribe has been nominated in HackerNoon's annual Startups of The Year awards in Gurgaon, India under the Programming category. Please vote for us here:
https://hackernoon.com/startups/asia/asia-gurugram-hr-india
https://hackernoon.com/startups/industry/saas https://hackernoon.com/startups/asia/asia-gurugram-hr-india?embedable=true Read more about us below to understand why we deserve your vote. Meet Forescribe Forescribe empowers enterprises to optimize their digital operations by providing real-time insights into SaaS usage, cloud infra, and tech adoption. With cutting-edge xAI features like Smart Alerts and AI Post Mortem, we help organizations enhance efficiency, reduce costs, and achieve compliance effortlessly. How Forescribe achieved product-market fit We’ve designed Forescribe to solve pain points that CIOs and IT leaders and FinOps professionals grapple with daily: skyrocketing tech costs, inefficient usage of SaaS tools, and a lack of visibility into tech ROI. Indicators of our product-market fit include:


Customer Retention: A steady stream of renewals and expanding use cases among clients.


Adoption by Leaders: Partnerships with high-profile clients in IT Services and HealthTech spaces.


Market Validation: The growing demand for Opex-focused solutions in SaaS management and cloud infrastructure, validated by direct feedback from IT and finance leaders.


Our approach resonates because it directly addresses business-critical challenges. The evolution of Forescribe Forescribe's evolution has been shaped by feedback and the changing needs of our customers. We initially focused on SaaS management, helping enterprises optimize software usage. As we engaged with users, we realized the need for a broader approach. Companies wanted insights that spanned SaaS, cloud, and tech ecosystems. This realization led to our pivot into a full-scale Digital Adoption Platform integrated with xAI capabilities. For example, feedback revealed a need for deeper compliance benchmarks, prompting the development of AI Post Mortem. Every pivot was informed by real-world challenges shared by our customers, ensuring that we always aligned our priorities with their needs. Milestones and achievements Enterprise Adoption: Partnered with marquee names, demonstrating trust and credibility in the market.


Innovation Launches: Introduced xAI features like Smart Alerts and AI Post Mortem, setting new standards in tech insights and optimization.


Recognition and Visibility: Secured a prominent presence on platforms like G2 and TrustRadius, bolstering our reputation.


Mobile App Rollout: Delivered a seamless on-the-go experience, empowering teams with real-time insights.


Data-Driven ROI Calculator: Made it easier for companies to quantify their potential savings and value using Forescribe.
One of our biggest challenges was building trust around data security and privacy in a landscape where concerns run high. Enterprises needed reassurance that their sensitive data would be handled responsibly. We tackled this by implementing robust security protocols, acquiring certifications, and maintaining transparent communication.The lesson? Trust is a cornerstone of adoption. Clear communication, empathy for customer concerns, and early investment in credibility-building measures are invaluable. What Startups of The Year means to us HackerNoon represents a vibrant community of innovators and tech enthusiasts who celebrate meaningful disruption. Participating allows us to showcase Forescribe’s capabilities to a global audience while connecting with like-minded individuals. We aim to generate awareness for our AI-powered solutions and build relationships that drive collaboration and growth. This initiative is not just about recognition—it’s about inspiring and being inspired to push boundaries in the tech management space. Conclusion At Forescribe, we believe that technology is an enabler, not a burden. Our mission is to simplify and optimize the management of digital ecosystems, empowering businesses to thrive in a tech-first world. With cutting-edge features, customer-centric evolution, and an unwavering focus on solving real problems, we are proud to lead the way in this transformative journey. The future of digital Opex optimization is here, and we’re just getting started. Vote for us today! Hey Hackers, Forescribe has been nominated in HackerNoon's annual Startups of The Year awards in Gurgaon, India under the Programming category . Forescribe Startups of The Year awards Programming category Please vote for us here: https://hackernoon.com/startups/asia/asia-gurugram-hr-india https://hackernoon.com/startups/industry/saas Please vote for us here: Please vote for us here: https://hackernoon.com/startups/asia/asia-gurugram-hr-india https://hackernoon.com/startups/asia/asia-gurugram-hr-india https://hackernoon.com/startups/industry/saas https://hackernoon.com/startups/industry/saas https://hackernoon.com/startups/asia/asia-gurugram-hr-india?embedable=true https://hackernoon.com/startups/asia/asia-gurugram-hr-india?embedable=true Read more about us below to understand why we deserve your vote. Meet Forescribe Forescribe empowers enterprises to optimize their digital operations by providing real-time insights into SaaS usage, cloud infra, and tech adoption. With cutting-edge xAI features like Smart Alerts and AI Post Mortem, we help organizations enhance efficiency, reduce costs, and achieve compliance effortlessly. Forescribe empowers enterprises to optimize their digital operations by providing real-time insights into SaaS usage, cloud infra, and tech adoption. With cutting-edge xAI features like Smart Alerts and AI Post Mortem, we help organizations enhance efficiency, reduce costs, and achieve compliance effortlessly. How Forescribe achieved product-market fit We’ve designed Forescribe to solve pain points that CIOs and IT leaders and FinOps professionals grapple with daily: skyrocketing tech costs, inefficient usage of SaaS tools, and a lack of visibility into tech ROI. Indicators of our product-market fit include:


Customer Retention: A steady stream of renewals and expanding use cases among clients.


Adoption by Leaders: Partnerships with high-profile clients in IT Services and HealthTech spaces.


Market Validation: The growing demand for Opex-focused solutions in SaaS management and cloud infrastructure, validated by direct feedback from IT and finance leaders.


Our approach resonates because it directly addresses business-critical challenges. We’ve designed Forescribe to solve pain points that CIOs and IT leaders and FinOps professionals grapple with daily: skyrocketing tech costs, inefficient usage of SaaS tools, and a lack of visibility into tech ROI. Indicators of our product-market fit include: Customer Retention: A steady stream of renewals and expanding use cases among clients.


Adoption by Leaders: Partnerships with high-profile clients in IT Services and HealthTech spaces.


Market Validation: The growing demand for Opex-focused solutions in SaaS management and cloud infrastructure, validated by direct feedback from IT and finance leaders. Our approach resonates because it directly addresses business-critical challenges. We’ve designed Forescribe to solve pain points that CIOs and IT leaders and FinOps professionals grapple with daily: skyrocketing tech costs, inefficient usage of SaaS tools, and a lack of visibility into tech ROI. Indicators of our product-market fit include: Customer Retention: A steady stream of renewals and expanding use cases among clients. Adoption by Leaders: Partnerships with high-profile clients in IT Services and HealthTech spaces. Market Validation: The growing demand for Opex-focused solutions in SaaS management and cloud infrastructure, validated by direct feedback from IT and finance leaders. Customer Retention: A steady stream of renewals and expanding use cases among clients. Customer Retention : A steady stream of renewals and expanding use cases among clients. Customer Retention Adoption by Leaders: Partnerships with high-profile clients in IT Services and HealthTech spaces. Adoption by Leaders : Partnerships with high-profile clients in IT Services and HealthTech spaces. Adoption by Leaders Market Validation: The growing demand for Opex-focused solutions in SaaS management and cloud infrastructure, validated by direct feedback from IT and finance leaders. Market Validation : The growing demand for Opex-focused solutions in SaaS management and cloud infrastructure, validated by direct feedback from IT and finance leaders. Market Validation Our approach resonates because it directly addresses business-critical challenges. The evolution of Forescribe Forescribe's evolution has been shaped by feedback and the changing needs of our customers. We initially focused on SaaS management, helping enterprises optimize software usage. As we engaged with users, we realized the need for a broader approach. Companies wanted insights that spanned SaaS, cloud, and tech ecosystems. This realization led to our pivot into a full-scale Digital Adoption Platform integrated with xAI capabilities. For example, feedback revealed a need for deeper compliance benchmarks, prompting the development of AI Post Mortem. Every pivot was informed by real-world challenges shared by our customers, ensuring that we always aligned our priorities with their needs. Milestones and achievements Enterprise Adoption: Partnered with marquee names, demonstrating trust and credibility in the market. Innovation Launches: Introduced xAI features like Smart Alerts and AI Post Mortem, setting new standards in tech insights and optimization. Recognition and Visibility: Secured a prominent presence on platforms like G2 and TrustRadius, bolstering our reputation. Mobile App Rollout: Delivered a seamless on-the-go experience, empowering teams with real-time insights. Data-Driven ROI Calculator: Made it easier for companies to quantify their potential savings and value using Forescribe.
One of our biggest challenges was building trust around data security and privacy in a landscape where concerns run high. Enterprises needed reassurance that their sensitive data would be handled responsibly. We tackled this by implementing robust security protocols, acquiring certifications, and maintaining transparent communication.The lesson? Trust is a cornerstone of adoption. Clear communication, empathy for customer concerns, and early investment in credibility-building measures are invaluable. Enterprise Adoption: Partnered with marquee names, demonstrating trust and credibility in the market. Enterprise Adoption : Partnered with marquee names, demonstrating trust and credibility in the market. Enterprise Adoption Innovation Launches: Introduced xAI features like Smart Alerts and AI Post Mortem, setting new standards in tech insights and optimization. Innovation Launches : Introduced xAI features like Smart Alerts and AI Post Mortem, setting new standards in tech insights and optimization. Innovation Launches Recognition and Visibility: Secured a prominent presence on platforms like G2 and TrustRadius, bolstering our reputation. Recognition and Visibility : Secured a prominent presence on platforms like G2 and TrustRadius, bolstering our reputation. Recognition and Visibility Mobile App Rollout: Delivered a seamless on-the-go experience, empowering teams with real-time insights. Mobile App Rollout : Delivered a seamless on-the-go experience, empowering teams with real-time insights. Mobile App Rollout Data-Driven ROI Calculator: Made it easier for companies to quantify their potential savings and value using Forescribe. One of our biggest challenges was building trust around data security and privacy in a landscape where concerns run high. Enterprises needed reassurance that their sensitive data would be handled responsibly. We tackled this by implementing robust security protocols, acquiring certifications, and maintaining transparent communication. The lesson? Trust is a cornerstone of adoption. Clear communication, empathy for customer concerns, and early investment in credibility-building measures are invaluable. Data-Driven ROI Calculator : Made it easier for companies to quantify their potential savings and value using Forescribe. Data-Driven ROI Calculator One of our biggest challenges was building trust around data security and privacy in a landscape where concerns run high. Enterprises needed reassurance that their sensitive data would be handled responsibly. We tackled this by implementing robust security protocols, acquiring certifications, and maintaining transparent communication. The lesson? Trust is a cornerstone of adoption. Clear communication, empathy for customer concerns, and early investment in credibility-building measures are invaluable. What Startups of The Year means to us HackerNoon represents a vibrant community of innovators and tech enthusiasts who celebrate meaningful disruption. Participating allows us to showcase Forescribe’s capabilities to a global audience while connecting with like-minded individuals. We aim to generate awareness for our AI-powered solutions and build relationships that drive collaboration and growth. This initiative is not just about recognition—it’s about inspiring and being inspired to push boundaries in the tech management space. Conclusion At Forescribe, we believe that technology is an enabler, not a burden. Our mission is to simplify and optimize the management of digital ecosystems, empowering businesses to thrive in a tech-first world. With cutting-edge features, customer-centric evolution, and an unwavering focus on solving real problems, we are proud to lead the way in this transformative journey. The future of digital Opex optimization is here, and we’re just getting started. Vote for us today! Vote for us today! Vote for us today! Vote for us today!

Meet Forescribe - A HackerNoon SOTY 2024 Nominee

Trust-based Currencies on Blockchain: Trustlines Network, Circles UBI, and Karma

Blockchain-də etimad əsaslı valyutalar: Trustlines Network, Circles UBI və Karma

Валюты на аснове даверу на блокчейне: сетка Trustlines, Circles UBI і Karma

ব্লকচেইনে বিশ্বাস-ভিত্তিক মুদ্রা: ট্রাস্টলাইন নেটওয়ার্ক, সার্কেল ইউবিআই, এবং কর্ম

Monedas basadas en la confianza en la cadena de bloques: Trustlines Network, Circles UBI y Karma

Luottamukseen perustuvat valuutat Blockchainissa: Trustlines Network, Circles UBI ja Karma

מטבעות מבוססי אמון בבלוקצ'יין: Trustlines Network, Circles UBI ו-Karma

ブロックチェーン上の信頼に基づく通貨: Trustlines Network、Circles UBI、Karma

Waluty oparte na zaufaniu w Blockchain: Trustlines Network, Circles UBI i Karma

Meny založené na dôvere na blockchaine: Trustlines Network, Circles UBI a Karma

Trust-based Currencies paBlockchain: Trustlines Network, Circles UBI, uye Karma

Асъорҳои боваринок дар Blockchain: Шабакаи Trustlines, Circles UBI ва Karma

Tiền tệ dựa trên sự tin cậy trên Blockchain: Trustlines Network, Circles UBI và Karma

5 Steps to Build an Exchange-Optimized L3 Rollup "Eventum"

5 pasos para crear un paquete acumulativo L3 optimizado para Exchange "Eventum"

5 koraka za izgradnju L3 skupnog paketa "Eventum" optimiziranog za Exchange

5 שלבים לבניית אוסף L3 המותאם ל-Exchange "Eventum"

Exchange に最適化された L3 ロールアップ「Eventum」を構築するための 5 つの手順

5 étapes ya kotonga "Eventum" ya Rollup L3 optimisée na échange.

5 ຂັ້ນຕອນເພື່ອສ້າງການແລກປ່ຽນແບບ L3 Rollup "Eventum" ທີ່ດີທີ່ສຸດ.

5 krokov na vybudovanie súhrnu L3 „Eventum“ optimalizovaného pre výmenu

Hatua 5 za Kuunda "Tukio" la L3 lililoboreshwa na Ubadilishanaji

5 Қадам барои сохтани як маҷмӯаи барои мубодилаи оптимизатсияшудаи L3 "Eventum"

Bir Exchangea-optimizirlenen L3 Rollup "Waka" gurmak üçin 5 ädim

Borsaya Optimize Edilmiş L3 Rollup "Eventum" Oluşturmak İçin 5 Adım

5 кроків для створення оптимізованого для Exchange зведеного пакета L3 "Eventum"

Stories, News & Mentions about X2AI

X2ai’s Stories on HackerNoon