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Cortex : AI COIN WITH TARGETSWe did add before about how important AI can be for the coming time, also since Elon musk did launch the known openAI software. This coin has to do with machining learning and this can affect new investors in the coming time, still, we should know it's crypto as there are no guarantees. What is our view on this coin? Our view on this coin is that this coin playing on very important trends, and we expect that we are going to see a new increase in trends. With the market cap and the supply of this coin, it's able to target $0,30, and with some new investments, this coin is able to go above 1 USD. This is our short view of it Targets $0,30, when this target gets confirmation high chance this coin is able to go above 1 USD. the best target for this coin can go $2.25 in the long-term view. As traders check the market with the day range. ( our view means not trading advice)
Long
by Goldenbreakout
Artificial Intelligence (AI): Trend and big playersThere is a lot of buzz around artificial intelligence (AI), as more and more companies and start-ups claim to be using it or developing AI-focused systems. In some cases, companies use old data analytics tools and label them as AI to boost public relations. But identifying companies, start-ups and projects that actually get revenue growth from AI systems integration or development can be difficult. But what really is AI? AI, or artificial intelligence, in a nutshell, refers to the simulation of human intelligence in machines programmed to think and act like humans. These machines are designed to learn, reason and make decisions just like humans and can be trained to perform a wide range of tasks, from games to driving cars. AI uses computer algorithms to replicate the human ability to learn and make predictions. The AI system needs computing power to find patterns and make inferences from large amounts of data. The two most common types of AI tools are called "machine learning" and "deep learning networks." What are the areas where AI is applicable? AI is a broad term. It can be used in many fields and contexts including health care, finance, education, transportation, art, and many others. Some common examples of AI applications are virtual personal assistants, facial recognition technology, autonomous vehicles, and systems for creating realistic images and artwork from a description (better known as a prompt). Key players in the AI scene There are many companies known for their work in the field of artificial intelligence. Among the most famous are Google, Microsoft, Facebook, Amazon, and Apple. These companies are known for their research and development in the field of artificial intelligence and for incorporating AI technology into their products and services. If we analyze the publicly traded companies, the circle narrows considerably, we list together the big players in the AI field: Nvidia (NVDA) is one company that can boast of AI-driven growth. Internet and technology companies are buying its processors for cloud computing. Nvidia's AI chips are also helping the development of self-driving cars in the early stages of testing. Startups are racing to build AI chips for data centers, robotics, smartphones, drones and other devices. Tech giants Apple (AAPL), Alphabet (GOOGL), Google's parent company, Facebook (FB) and Microsoft (MSFT) have made strides in applying AI software-from speech recognition to Internet search and image classification and development. Amazon.com's artificial intelligence especially extends to cloud computing services and voice-activated home digital assistants. Then there are technology companies that incorporate AI tools into their products to improve them. These include video streamer Netflix (NFLX), payment processor PayPal (PYPL), Salesforce.com (CRM), and again Facebook. Customers of technology companies spanning banking and finance, healthcare, energy, retail, agriculture, and other sectors are expected to increase investments and allocate new funds for AI in order to gain productivity gains and/or a strategic advantage over rivals. In addition to the companies mentioned above, one of the leading players in AI systems development is OpenAI (no, it is not publicly traded). OpenAI is an artificial intelligence research institute and laboratory founded in 2015. It is dedicated to advancing and promoting AI research and development in a safe and responsible manner. The organization is known for developing AI algorithms and systems capable of achieving human-like intelligence. OpenAI is a nonprofit organization supported by a number of high-profile donors and sponsors, including Elon Musk and the Chan Zuckerberg Initiative. The revolutionary tools of OpenAI Among OpenAI's most important achievements is the development of the GPT-3 language model, which has been widely used in natural language processing applications. Currently, it is already possible to test the chat at the "research preview" stage on the main site, putting it to the test by proposing complex themes and topics, such as programming languages, algorithms, or simple advice on how to furnish a house. Another revolutionary tool, proposed by the nonprofit organization, is DALL-E. DALL-E is a large language model that has been "trained" by OpenAI. It can generate images from textual descriptions, using a neural network with 14 billion parameters. DALL-E uses a combination of natural language processing and computer vision techniques to generate highly detailed and imaginative images. For example, when prompted for the text "A bird with the body of a giraffe and the head of a parrot," DALL-E could generate an image of a giraffe with the head of a parrot...simpler than that! Digital ART and NFT DALL-E has enabled many designers and artists to be able to create very complex artwork and works, resulting in incredible results with the simple development of a detailed description, all in very little time. While still little mentioned in the media and little used by retailers, we have already seen a fair amount of interest arising from artists, especially in the area of digital art and NFT. The current NFT market, although in a bearish phase, has seen a remarkable increase in volumes in the last week. What is curious is that in the top 100 ranking of the highest volume projects on OpenSea (the number one marketplace for buying and selling NFT), 40% are generative or AI-made art collections, with some sales exceeding 65 Ethereum ($80K+). In addition to art collections, exciting projects have sprung up using blockchain technology combined with AI systems proposed by OpenAi and beyond. One example is 0xAI, a startup on the ethereum blockchain that provides its users with the most powerful AI systems for creating digital works, greatly simplifying the process of use and adoption. Native blockchain and non-onchain startups using artificial intelligence will soon be the order of the day. Although the potential is obvious, it is necessary to analyze the foundations of the projects, the products offered and their growth prospects, as it is easy to create an extremely saturated and insolvent market. Conclusion The AI revolution has just begun, we are at the beginning of a new era where technology as we are used to seeing it could "mutate" significantly and it is already happening. Leading technology companies have long shown the interest, desire and need to convert to AI systems, both to facilitate the productivity process and thus save funds in the medium/long term, and to capture the interest of new potential investors. We will closely monitor developments in this new and intriguing branch of modern technology.
NASDAQ:GOOGL
by cryptohubble
1
Air Liquide has formed a bull flag!Air Liquide - 30d expiry - We look to Buy a break of 134.52 (stop at 129.88) Posted a Double Bottom formation. Prices have reacted from 114.44. Posted a bullish Flag formation. A break of 134.30 is needed to confirm the outlook. The bias is to break to the upside. Our outlook is bullish. The primary trend remains bullish. Our profit targets will be 145.88 and 149.88 Resistance: 134.30 / 138.00 / 142.50 Support: 130.36 / 127.00 / 122.50 Disclaimer – Saxo Bank Group. Please be reminded – you alone are responsible for your trading – both gains and losses. There is a very high degree of risk involved in trading. The technical analysis , like any and all indicators, strategies, columns, articles and other features accessible on/though this site (including those from Signal Centre) are for informational purposes only and should not be construed as investment advice by you. Such technical analysis are believed to be obtained from sources believed to be reliable, but not warrant their respective completeness or accuracy, or warrant any results from the use of the information. Your use of the technical analysis , as would also your use of any and all mentioned indicators, strategies, columns, articles and all other features, is entirely at your own risk and it is your sole responsibility to evaluate the accuracy, completeness and usefulness (including suitability) of the information. You should assess the risk of any trade with your financial adviser and make your own independent decision(s) regarding any tradable products which may be the subject matter of the technical analysis or any of the said indicators, strategies, columns, articles and all other features. Please also be reminded that if despite the above, any of the said technical analysis (or any of the said indicators, strategies, columns, articles and other features accessible on/through this site) is found to be advisory or a recommendation; and not merely informational in nature, the same is in any event provided with the intention of being for general circulation and availability only. As such it is not intended to and does not form part of any offer or recommendation directed at you specifically, or have any regard to the investment objectives, financial situation or needs of yourself or any other specific person. Before committing to a trade or investment therefore, please seek advice from a financial or other professional adviser regarding the suitability of the product for you and (where available) read the relevant product offer/description documents, including the risk disclosures. If you do not wish to seek such financial advice, please still exercise your mind and consider carefully whether the product is suitable for you because you alone remain responsible for your trading – both gains and losses.
EURONEXT:AILong
by Saxo
LONG AGIXUSDT SPOT TRADE FOR ACCUMULATION BUT ADDED TRADE IDEAThis is one of my favorite cryptos. $AGIX is an AI based project that has incredible fundamentals. I put a trade setup but could care less about it because I will keep accumulating as many bags of this as possible for next cycle. I do feel this trade will be profitable though but the better bet is to hodl it and keep adding. Sell at 50-100x or more next bull run. Of course take profits along the way but this is a killer project. Everyone should do your own research but I think you will agree this is a true crypto GEM. No leverage just spot and is on KuCoin on the USDT and ETH Pairing. Not financial advice but grab some moon bags and hodl this token!
ALong
by Z3R0-G3N3S1S
1
Air Liquide drops should attract buyers.Air Liquide - 30d expiry - We look to Buy at 115.12 (stop at 111.78) The primary trend remains bullish. Intraday dips continue to attract buyers and there is no clear indication that this sequence for trading is coming to an end. Early pessimism is likely to lead to losses although extended attempts lower are expected to fail. Support is located at 115 and should stem dips to this area. We are trading at oversold extremes. Our profit targets will be 123.88 and 126.88 Resistance: 120 / 125 / 130 Support: 115 / 110 / 105 Weekly perspective Disclaimer – Saxo Bank Group. Please be reminded – you alone are responsible for your trading – both gains and losses. There is a very high degree of risk involved in trading. The technical analysis , like any and all indicators, strategies, columns, articles and other features accessible on/though this site (including those from Signal Centre) are for informational purposes only and should not be construed as investment advice by you. Such technical analysis are believed to be obtained from sources believed to be reliable, but not warrant their respective completeness or accuracy, or warrant any results from the use of the information. Your use of the technical analysis , as would also your use of any and all mentioned indicators, strategies, columns, articles and all other features, is entirely at your own risk and it is your sole responsibility to evaluate the accuracy, completeness and usefulness (including suitability) of the information. You should assess the risk of any trade with your financial adviser and make your own independent decision(s) regarding any tradable products which may be the subject matter of the technical analysis or any of the said indicators, strategies, columns, articles and all other features. Please also be reminded that if despite the above, any of the said technical analysis (or any of the said indicators, strategies, columns, articles and other features accessible on/through this site) is found to be advisory or a recommendation; and not merely informational in nature, the same is in any event provided with the intention of being for general circulation and availability only. As such it is not intended to and does not form part of any offer or recommendation directed at you specifically, or have any regard to the investment objectives, financial situation or needs of yourself or any other specific person. Before committing to a trade or investment therefore, please seek advice from a financial or other professional adviser regarding the suitability of the product for you and (where available) read the relevant product offer/description documents, including the risk disclosures. If you do not wish to seek such financial advice, please still exercise your mind and consider carefully whether the product is suitable for you because you alone remain responsible for your trading – both gains and losses.
EURONEXT:AILong
by Saxo
When Will Robots Gain ‘Common Sense’?2022 has been a year where we have heard different applications of artificial intelligence continually increasing different types of capabilities: - Large-language-models, exemplified by GPT-31, have become larger and have pointed their capabilities toward more and more areas, like computer programming languages. - DeepMind further expanded its AlphaFold toolkit, showing predictions of the structure of more than 200 million proteins and making these predictions available at no charge for researchers2. - There has even been expansion in what’s termed ‘autoML’3 , which refers to low-code machine learning tools that could give more people, without data science or computer science expertise, access to machine learning4. However, even if we can agree that advances are happening machines are still primarily helpful in discrete tasks and would not possess much in the way of flexibility to respond to many different changing situations in short order. Intersection of large language models and robotics Large language models are interesting in many cases for their emergent properties. These giant models may have hundreds of billions if not trillions of parameters. One output could be written text. Another could be something akin to ‘autocomplete’ in coding applications. But what if you told a robot something like, ‘I’m hungry.’ As a human being, if we hear someone say, ‘I’m hungry’, we can intuit many different things quite quickly based on our surroundings. At a certain time of day, maybe we start thinking of going to restaurants. Maybe we get the smartphone out and think about takeout or delivery. Maybe we start preparing a meal. A robot would not necessarily have any of this ‘situational awareness’ if it wasn’t fully programmed in ahead of time. We would naturally tend to think of robots as able to perform their specific, pre-programmed functions within the guidelines of precise tasks. Maybe we would think a robot could respond to a series of very simple instructions—telling it where to go with certain key words, what to do with certain additional key words. ‘I’m hungry’—a two-word command with no inherent instructions would be assumed to be impossible. Google’s Pathways Language Model (PaLM) — a start to more complex human/robot interactions Google researchers were able to demonstrate a robot able to respond, within a closed environment admittedly, to the statement ‘I’m hungry.’ It was able to locate food, grasp it, and then offer it to the human5. Google’s PaLM model was underlying the robot’s capability to take the inputs of language and translate them into actions. PaLM is notable in that it builds in the capability to explain, in natural language, how it reaches certain conclusions6. As is often the case, the most dynamic outcomes tend to come when one can mix different ways of learning to lead to greater capabilities. Of course, PaLM, by itself, cannot automatically inform a robot how to physically grab a bar of chocolate, for example. The researchers would demonstrate, via remote control, how to do certain things. PaLM was helpful in allowing the robot to connect these concrete, learned actions, with relatively abstract statements made by humans, such as ‘I’m hungry’ which doesn’t necessarily have any explicit command7. The researchers at Google and Everyday Robots titled a paper ‘Do As I Can, Not As I Say: Grounding Language in Robotic Affordance’s 8. In Figure 1, we see the genius behind such a title, in that it’s important to recognise that large language models may take as their ‘inspiration’ text from across the entire internet, most of which would not be applicable to a particular robot in a particular situation. The system must ‘find the intersection’ between what the language model indicates makes sense to do and what the robot itself can actually achieve in the physical world. For instance: - Different language models might associate cleaning up a spill with all different types of cleaning—they may not be able to use their immense training to see that a vacuum may not be the best way to clean up a liquid. They may also simply express regret that a spill has occurred. - If one is thinking of the intersection that has the highest chance of making sense, if a robot in a given situation can ‘find a sponge’ and the large language model indicates that the response of ‘find a sponge’ could make sense, marrying these two concepts could lead the robot to at least attempt a productive, corrective action to the spill situation. The ‘SayCan’ model, while certainly not perfect and not a substitute for true understanding, is an interesting way to get robots to do things that could make sense in a situation without being directly programmed to respond to a statement in that precise manner. In a sense, this is the most exciting part of this particular line of research: - Robots tend to need short, hard-coded commands. Understanding more less specific instructions isn’t typically possible. - Large language models have demonstrated an impressive capability to respond to different prompts, but it’s always in a ‘digital-only’ setting. If the strength of robots in the physical world can be married with the, at least seeming, capability to understand natural language that comes from large language models, you have the opportunity for a notable synergy that is better than either working on its own. Conclusion: companies are pursuing robotic capabilities in a variety of ways Within artificial intelligence, it’s important to recognise the critical progression from concept to research to breakthroughs and then only later mass market usage and (hopefully) profitability. The robots understanding abstract natural language today could be some distance away from mass market revenue generating activity. Yet, we see companies taking action toward greater and greater usage of robotics. Amazon is often in focus for what it may be able to use robots for in its distribution centres, but even more recently it has announced its intention to acquire iRobot9, the maker of the Roomba vacuum system. Robots with increasingly advanced capability will have a role to play in society as we keep moving forward. Today’s environment of rising wage pressures does have companies exploring more and more what robots and automation could bring to their operations. It is important not to overstate where we are in 2022—robots are not able to exemplify fully human behaviours at this point—but we should expect remarkable progress in the coming years. Sources 1 Generative Pre-trained Transformer 3 2 Source: Callaway, Ewen. “’The Entire Protein Universe’: AI Predicts Shape of Nearly Every Known Protein.” Nature. Volume 608. 4 August 2022. 3 Automated machine learning 4 Source: Xu, Tammy. “Automated techniques could make it easier to develop AI.” MIT Technology Review. 5 August 2022. 5 Source: Knight, Will. “Google’s New Robot Learned to Take Orders by Scraping the Web.” WIRED. 16 August 2022. 6 Source: Knight, 16 August 2022. 7 Source: Knight, 16 August 2022. 8 Source: Ahn et al. “Do As I Can, Not As I Say: Grounding Language in Robotic Affordances.” ARXIV. Submitted 4 April 2022, last revised 16 August 2022. 9 Source: Hart, Connor. “Amazon Buying Roomba Maker iRobot for $1.7 Billion.” Wall Street Journal. 5 August 2022. This material is prepared by WisdomTree and its affiliates and is not intended to be relied upon as a forecast, research or investment advice, and is not a recommendation, offer or solicitation to buy or sell any securities or to adopt any investment strategy. The opinions expressed are as of the date of production and may change as subsequent conditions vary. The information and opinions contained in this material are derived from proprietary and non-proprietary sources. As such, no warranty of accuracy or reliability is given and no responsibility arising in any other way for errors and omissions (including responsibility to any person by reason of negligence) is accepted by WisdomTree, nor any affiliate, nor any of their officers, employees or agents. Reliance upon information in this material is at the sole discretion of the reader. Past performance is not a reliable indicator of future performance.
A
by aneekaguptaWTE
AI continues to build the foundation for a remarkable future in 28 July 2022 was an historic day in both biology and artificial intelligence (AI). DeepMind, a firm specialising in AI research owned by Alphabet, made freely available the structural data on more than 200 million proteins from its AlphaFold tool. This represents data on roughly 1 million species and covers the vast majority of known proteins on earth1. In proteins, shape can determine function In the late 1990’s into the early 2000’s, the scientific community was awash with news of the race to sequence the human genome. This genome contains the instructions embedded in DNA about how cells should build certain structures, typically through the formation of proteins that are made from different combinations of amino acids. In a sense, DNA is the instruction manual, amino acids are the building blocks, and proteins are the product. Knowing the code, however, is not the full story. Looking at figure 1 is instructive on the point. This is the image of a protein that may protect the organism responsible for malaria from an attack by the human immune system. Even if you knew the list and the order of all the amino acids, it would be difficult to go from that list to something that looks like figure 1 in three dimensions. The importance of the shape of the protein could not be overstated: It can correspond to the way in which it might react in the presence of different molecules, like those associated with different drug therapies Variations on the shape—sometimes termed mutations—could be instructive in determining the causal factors of certain symptoms or diseases Parts of the shape could be used as targets—think of the ‘spike protein’ associated with the virus behind the Covid-19 pandemic, specifically targeted within the mRNA vaccines. AlphaFold Represents a Leap Forward on the Journey Scientific breakthroughs are difficult, in that in many cases one builds on another and another and another…a process that can take decades prior to widespread results that impact the lives of the general public. For instance—we sequenced the human genome, but that did not necessarily lead to immediate cures of all sorts of diseases or conditions. mRNA2 research had been occurring for decades, but the Covid-19 pandemic was somewhat of a catalyst to supercharge the process to use it as a case for vaccines. AlphaFold’s new database is therefore unlikely to lead to immediate cures for difficult conditions. The critical element with regards to how researchers that would have formerly had to undertake a cumbersome process of X-ray crystallography to determine the shape of a given protein could instead go to the database. Experimental techniques would still have their place, but less time would have to be spent on the equivalent of the ‘blank page.’ What’s also incredible is that AlphaFold’s database is, in conjunction with the European Molecular Biology Laboratory’s European Bioinformatics Institute (EMBL-EBI) freely available with a simple interface. It also provides an estimate of the accuracy, recognising that predictions based on AI do not yield perfect results all of the time. Roughly 35% of the 214 million predictions are deemed highly accurate—roughly as good as experimental results. A further 45% are deemed to be accurate enough for many applications3. Drug discovery—Better therapeutics developed more efficiently Even before the onset of inflation at the levels we see in the summer of 2022, it was widely recognised that drug development was time consuming and expensive, and as a result many different medications carried with them exorbitant price tags. Any process that could mitigate this pressure without degrading the quality of the therapies would be valuable. Considering the following could be instructive as the space continues to progress4: Pipeline growth: 20 smaller companies focused on AI drug discovery, typically with a focus on smaller molecules, over a period from 2010 to 2021, had development pipelines that were roughly 50% as robust as those of 20 of the largest ‘big pharma’ companies. We recognise that the reporting of pipelines may not be perfect and that a molecule in a pipeline is not a finished product, but activity is the first step on the path Pipeline composition: This is not always disclosed, but from the information available it does indicate that the AI-focused companies will tend to focus on well established biological targets for their therapies, around which much is known. Data is the fuel for AI, and these companies will also want higher chances of success. Bigger pharma companies will be more likely to venture into more emerging areas of drug discovery Chemical structures and properties: It is too early to be able to draw any robust conclusions regarding AI drug discovery efforts versus big pharma efforts at this point Discovery Timelines: Preliminary data would appear to indicate that, if traditional approaches would tend to take 5 or 6 years in preclinical phases, AI-focused drug discovery might be able to, in certain cases, take this timeline down to 4 years We’d note that currently it’s a story of more ‘progress’ than ‘perfection’, in that we would appear to be some distance away from AI being able to fully create new drugs, but AI is representing an entirely new set of tools that could have beneficial impacts. AlphaFold’s database, for example, may provide drug researchers with important inputs and catalysts for different ideas, even if it doesn’t have the immediate answer or cure right there in its system. Focus on the AI & BioRevolution megatrends At WisdomTree, we focus on both the AI and the BioRevolution megatrends (click to find out more). What we see here with the case of AlphaFold is an important case study in the fact that AI is a tool that can have the potential to supercharge other megatrends, in this case the BioRevolution. It is no accident that the BioRevolution is ramping up at the same time there are massive amounts of data, massive amounts of processing power and other things like cloud computing readily available. It is very exciting to consider what the coming decades can bring within these areas. Sources 1 Source: Callaway, Ewen. “’The Entire Protein Universe’: AI Predicts Shape of Nearly Every Known Protein.” Nature. Volume 608. 4 August 2022. 2 mRNA – Messenger ribonucleic acid 3 Source: Callaway, 4 August 2022. 4 Source: Jayatunga et al. “AI in small-molecule drug discovery: a coming wave?” Nature Review: Drug Discovery. Volume 21. March 2022. This material is prepared by WisdomTree and its affiliates and is not intended to be relied upon as a forecast, research or investment advice, and is not a recommendation, offer or solicitation to buy or sell any securities or to adopt any investment strategy. The opinions expressed are as of the date of production and may change as subsequent conditions vary. The information and opinions contained in this material are derived from proprietary and non-proprietary sources. As such, no warranty of accuracy or reliability is given and no responsibility arising in any other way for errors and omissions (including responsibility to any person by reason of negligence) is accepted by WisdomTree, nor any affiliate, nor any of their officers, employees or agents. Reliance upon information in this material is at the sole discretion of the reader. Past performance is not a reliable indicator of future performance.
NASDAQ:NQINTEL
by aneekaguptaWTE
3
Automatic Gamma Levels Spot alternative on TradingView? Sure!By combining Options expiration data, DarkPool & process it by Machine Learning algorithm, we can get another perspective of market picture. One of the services is already trying to provide this data, however so far it requires manual work in order to apply those levels on the chart. I provide a way for automatic level recognition valid for intraday session, based on data calculated by ML/AI algorithm, exported to Quandl database and imported to TradingView indicator. When you add to that Call & Put Wall levels I publish on website, you will have all information in hand that you can use for intraday trading. I provide current for the moment of writing analysis snapshot of 4 majors with applied Gamma Levels as well as key levels from Options&Darkpool data market. If you want to learn more and start having edge on the market (information is the key!), you will for sure find more details at my profile ;) I wish you all Big Profits on the market!
NYMEX:CL1!
by skyline-trader
2
Upcoming $VSBGF Earnings - Price TargetNew company with a very bright future. VSBLTY Groupe Technologies Corp $VSBGF earnings report is expected on May 4th. The company has just begun to turn profits, signed numerous big name agreements with global companies, and continues to present a very bright future. Sensors & displays in retail sales environments, artificial intelligence, security... this is a sleeping giant. November 21 earnings resulted in >30% spike in the face of broader market downturn and macroeconomic headwinds pulling the company down. The more broad negative market sentiment presents a rare opportunity to continue to load up on shares prior to the next earnings report on 4 May. The stock is significantly undervalued with revenue projected to increase >45% this year PE of -6.7x, market PE is 44.5x & the broader market is 12.6x As a nascent company with a strong pipeline and implementations on track, the PB rating of 17.8x is just scratching the surface as it has just a 16% debt to equity ratio.
OTC:VSBGFLong
by R0MM3LL
Fall potential on Gold #options #ai #mlOption data and AI algorithm from analysis data from this market show the possibility of retreat from growth and switch to Bears taking control over. We have a powerful resistance (supply zone) around 2060, and Virgin VPOCs can be found only below the current price level (for a moment of writing analysis). Current option support is 1881 and this is a key level when it comes to potential closure of the downward movement. To Gamma Flip (whose exceeding will increase the volatility on the market) still far (1786), but it is worth observing what will happen on subsequent sessions. Falls will be negated after exceeding 1975. I take into account all Expiration from the gold option, which are then treated by the Machine Learning script. AI in this case shows the main key levels on the market and conclusions from data analysis. They are exported to the Quandl base and then imported to TradingView. Data is also published every day a week, on my website. Remember - knowledge and data are power, in this case, increasing significantly a chance for profitable trading :)
COMEX:GC1!
by skyline-trader
C3.ai Inc (NYSE: $AI) Bottom'd & Ready For Robot Revolution! 🤖C3.ai, Inc. operates as an enterprise artificial intelligence (AI) software company in North America, Europe, the Middle East, Africa, the Asia Pacific, and internationally. It offers software-as-a-service applications for enterprises. Its software solutions include C3 AI Suite, a platform-as-a-service application development and runtime environment that enables customers to design, develop, and deploy enterprise AI applications; C3 AI Ex Machina to analysis-ready data; and C3 AI CRM, an AI-first customer relationship management solution to drive customer-facing operations. It also offers C3 AI applications, including C3 AI Inventory Optimization, a solution to optimize raw material, in-process, and finished goods inventory levels; C3 AI Supply Network Risk, which provides visibility into risks of disruption throughout the supply chain operations; C3 AI Customer Churn Management, which enables account executives and relationship managers to monitor customer satisfaction, as well as to prevent customer churn with AI-based and human-interpretable predictions and warning; C3 AI Production Schedule Optimization, a solution for scheduling production; C3 AI Predictive Maintenance, which provides insight into asset risk to maintenance planners and equipment operators; C3 AI Fraud Detection solution that identify revenue leakage or maintenance and safety; and C3 AI Energy Management solution. In addition, it offers integrated turnkey enterprise AI applications for oil and gas, chemicals, utilities, manufacturing, financial services, defense, intelligence, aerospace, healthcare, and telecommunications market segments. It has strategic partnerships with Baker Hughes in the areas of oil and gas market; FIS in the areas of financial services market; Raytheon; and AWS, Intel, and Microsoft. The company was formerly known as C3 IoT, Inc. and changed its name to C3.ai, Inc. in June 2019. C3.ai, Inc. was incorporated in 2009 and is headquartered in Redwood City, California.
NYSE:AILong
by Bullishcharts
3