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Artificial_intelligence

Oceanusdt AI AI has shown what it is capable of in last few month. Most of the crypto coins associated with AI have formed the bottom and have nice zone of accumulation. OCEAN is main AI crypto protocol today in crypto. What if right now is forming next trend of 2023 year in crypto and we just blinded with bearishnes of market. Who expect for some coins make x10 in next 6 month, i think nobody. some info: 637 days from ATH 1.9 in april 2021 231 days in accumulation zone Botoomed at 0.12 ICO price was 0.12 my targets: TP1 0.55 TP2 0.95 TP3 1.25 TP4 1.9 if we brake i cant imagine whats price go next.
OLong
by BigCryptoUA
1
Is MARSH Going For It's ATH of 30X!?? Same Like AI Coins?!KUCOIN:MARSHUSDT MARSH is Same Project as GRT (The graph) Market Cap of GRT is $1,166,562,295 ($1.1 Billion) and ranked at 46 CoinMarketCap Now, MARSH has just $1,390,317($1.4 Million) of Marketcap and ranked at 1345 on CoinMarketCap As we know that this whole bull market in crypto right now is driven by AI narrative and GRT is also playing a role in this narrative. So at least MARSH Should get 100-200 Million $ Market Cap Now, lets Look at the Charts: MARSH Broke out from the big downtrend and now started a new cycle. MARSH was in accumulation for 245 days and currently it broke out from that accumulation, retested it and now going for a parabolic move ahead For Now the Targets should be at $0.26-$0.43 and $0.65 If we see this whole move as a parabola and the narrative of managing the Big data gets hyped , MARSH can touch it's previous All Time Highs Targets for that would be $1.6-$1.8 As we should always have an invalidation in our setup so to exit from the coin and to avoid total loss of capital Invalidation level would be Daily candle closing below $0.1 On the Other Hand GRT has made a breakout and can go for minimum 2x from current market price which will push the demand for alternative projects of GRT..which is MARSH to have a exponential hype growth in this bull run. Thank you reading :) Please follow and like the post as i hope i gave you some value read 👍
Long
by Traders_skooll
2
FET|USD Soars 326% in 30 Days: A Rapid Rise ReflectionsFetch.ai is a decentralized platform that provides AI-powered solutions for various industries, including finance, transportation, and energy. It uses blockchain technology to allow for secure and efficient exchanges of data and value, enabling the creation of autonomous economic agents. Fetch.ai offers a range of products, including a decentralized marketplace for data exchange, a digital twin platform for modeling real-world systems, and a suite of AI tools for businesses and developers. The platform enables efficient, secure, and decentralized transactions between individuals and organizations, allowing for a more efficient and fair exchange of value. Fetch.ai's vision is to create a decentralized, self-organizing, and autonomous digital economy, where data, value, and tasks are managed and executed by autonomous agents, allowing for more efficient and effective transactions. By leveraging the power of AI and blockchain technology, Fetch.ai aims to revolutionize traditional industries and create new opportunities for businesses and individuals. A 1-day chart analysis of Fetch.ai (FET) can provide valuable insights for short-term holders of the cryptocurrency. By examining the price trends, volatility, and volume of Fetch.ai over the past day, it is possible to make predictions about the short-term performance of the asset. For instance, a sudden surge in the price of Fetch.ai, accompanied by high trading volume, can indicate a sudden increase in demand for the asset, which can be a good opportunity for short-term traders to take advantage of the price increase and sell at a profit. On the other hand, if the chart shows a lot of volatility and price swings, it could signal a lack of market stability, which may be a concern for short-term holders. Additionally, a 1-day chart analysis can also provide insights into the impact of any major events or news on the price of Fetch.ai. For example, if there was a significant spike in price following the announcement of a new partnership or product launch, this may indicate that the market is optimistic about the potential of the project and that short-term holders may benefit from this positive sentiment. Overall, a 1-day chart analysis can provide valuable information for short-term Fetch.ai holders to make informed decisions about their investments. By monitoring trends and staying informed about market conditions, they can make more informed decisions about buying and selling Fetch.ai.
BINANCEUS:FETUSD
by SuzanneSpisak
1
SOUN | A.I. Play Oversold | LONGSoundHound AI, Inc. develops independent voice artificial intelligence (AI) platform that enables businesses across industries to deliver high-quality conversational experiences to their customers. Its products include Houndify platform that offers a suite of Houndify tools to help brands build conversational voice assistants, such as automatic speech recognition, natural language understanding, wake words, custom domains, text-to-speech, and embedded voice solutions The company is headquartered in Santa Clara, California.
NASDAQ:SOUNLong
by DivergenceSeeker
3
TOP ASSETS of the AI NARRATIVE | PART 2In the comments of “Top AI assets part 1” you mentioned some more promising projects, the main product of which is AI. We decided to tell you more about them and check their metrics iExec RLC iExec is considered as a project with the AI narrative, but it is partly wrong. The main specialization of iExec is providing computing power and organizing the market around this sector. iExec forms large volumes of data and if we check their products, we will see that these volumes of data are being used actively but we have to understand that this is a side line of their business. In general, iExec as a project is more like Flux than any project in the AI narrative. Metrics of the $RLC token: Price: $1.75 ATH price: $11.6 Market.cap: $141m ATH market.cap: $800m FDMC: $152m Over the past 2 months, the $RLC token has grown more than 2 times. We do like iExec as a project with its own goals and values and that’s why we listed it on our platform for trading Vectorspace AI The team focuses on creating AI and ML solutions in space biosciences, general life science and capital markets. So far the team has launched two products: A financial product for protecting investment portfolios and finding stock and cryptocurrency market correlations for long or short trades. A product for biosciences in a Protein Relationship Networks area. Metrics of $VXV token: Price: $0.57 ATH price: $18.1 Market.cap: $27m ATH market.cap: $347m FDMC: $28m Over the past 2 months, the $VXV token has grown more than 2 times. Matrix AI Network Project that focuses on an AI integration directly into the crypto. Matrix has 4 main products: Mania - a platform for trading AI algorithms in an NFT type Airtist - a generative art creation platform for NFT Manta - an automatic machine learning platform Matrix - an AI service platform Metrics of $MAN token: Price: $0.02254 ATH price: $1.7 Market.cap: $4.8m ATH market.cap: $6m FDMC: $22.5m Over the past 2 months, the $MAN token has grown more than 4 times. Numeraire Platform for Data Science and Machine Learning specialists. Project supports DS and ML specialists, conducts predictive ML contests and builds its own progressive community. Metrics of $NMR token: Price: $16.7 ATH price: $84 Market.cap: $98m ATH market.cap: $487m FDMC: $183m Over the past 2 months $NMR has grown by 64% Streamr A project for data transferring within web3. Streamr is primarily an infrastructure project, preparing the basis for the data economy. Metrics of $DATA token: Price: $0.03308 ATH price: $0.3102 Market.cap: $25m ATH market.cap: $223m FDMC: $28m Over the past 2 months $DATA has grown by 50% Conclusion As we’ve told you earlier, the benefits that AI offers, along with its increasing adoption and application, guarantee the expansion of AI projects and a profitable market. Let us know in the comments about more AI projects we should look at. Share your investing or trading experience with such projects.Thanks for reading!
Education
by Samura0x
6
MATRIX AI: IS THIS SLEEPING GIANT OF Artificial IntelligenceThose who follow us know that we have a huge interest in AI coins and with time we try to find the best possibility for machine learning coins AI. We believe that AI can play a very important role in markets coming time. Elon musk already did the first step with OPENAI This coin can be a sleeping giant and is able to break out coming time. We did scan these coins as possible that could do great trends. We will follow it and see if it can give some confirmations coming time. MATRIX 3.0 coming soon
Long
by Goldenbreakout
13
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
Will AI help us in building better batteries?We have written a series of blogs on how artificial intelligence (AI) is advancing other megatrends: AI Continues to Build the Foundation for a Remarkable Future in Biology Can AI Replace People? The Truck-Driving Case Study The World Needs More Metals. Maybe AI can Find Them. By exploring these connections between themes, we can view AI less as a black box of algorithmic complexity and more as something that is focused on solving concrete problems in the world. A brief primer on electrochemical batteries1 What we know today as ‘lithium-ion’ batteries fall into the class of ‘electrochemical batteries’. For the battery to generate power the chemical process has to generate electrons, and for the battery to be ‘re-charged’ it has to store electrons. The structure of the battery involves the anode (negative side), electrolyte and cathode (positive side). The current that the battery can generate relates to the number of electrons flowing across from negative to positive, and the voltage relates to the force with which the electrons are traveling. Using the battery, that is, using your smartphone or driving your electric car, means that the electrons are flowing from the anode, through the electrolyte and to the cathode. Charging your devices means that you are forcing the process to occur in reverse, where the electrons are leaving the cathode, going back across the electrolyte and ending up in the anode. Why do we have to know all of that? Some of you might be like me and think—my last chemistry class was more than 20 years ago. The reason we set that foundation, however, is that it now allows us to think in terms of the following: The different parts of the battery can be fashioned out of different elements. Changing the mix of metals in the cathode, for example, may impact the energy density, speed of charging, heat dispersion or other battery characteristics. Researchers can experiment with all sorts of different anodes, cathodes and electrolytes as they seek to optimise the characteristics of a given battery to its use case. Now we can better understand the ways in which an artificial intelligence process can be utilised to seek to improve different characteristics of the batteries that we use. Who wants electric vehicles to charge faster? One of the many obstacles to the wider usage of electric vehicles is the time it takes to charge a battery vs. filling a tank with petrol. Since filling the tank is much faster, they opt for the internal combustion engine over the battery electric vehicle. There is huge marketability for automobile manufacturers and battery-makers for every unit of time they can shave off of charging times. Researchers at Carnegie Mellon used a robotic system to run dozens of experiments designed to generate different electrolytes that could enable lithium-ion batteries to charge faster. The system is known as Clio, and it was able to both mix different solutions together as well as measure performance against critical battery benchmarks. These results were then fed into a machine-learning system, known as Dragonfly2. Dragonfly is where the process starts to get exciting—the system is designed to propose possible combinations of chemicals to be used in the electrolytes that could potentially work even better. Using this process during this particular time period led to six different electrolyte solutions that outperformed a standard one when they were placed into typical battery test cells. The best option showed a 13% improvement relative to the top-performing battery baseline3. In reality, electrolyte ingredients can be mixed and matched billions of different ways, but the benefit of using the system of Clio and Dragonfly working together is that one can get through a wider array of possibilities faster than humans alone. Dragonfly also isn’t equipped with information about chemistry or batteries, so it doesn’t bring the ‘bias of previous knowledge or experience’ to the process. Using AI to help the progress of solid-state batteries While the aforementioned path involves improving liquid electrolytes, it is not the only critical area of battery research today. If the flammable, liquid electrolyte is replaced by a stable solid, it’s possible that there would be improvements in battery safety, lifetime and energy density. However, finding the appropriate materials to facilitate building solid-state batteries that fit all specifications and that can be produced at scale is not a simple matter. Researchers at Stanford have noted a particular process where they compile data on 40 materials with both good and bad measured room temperature lithium conductivity values. This particular characteristic is thought to be the most restrictive of all the different constraints on candidate materials. The 40 examples are ‘shown’ to a logistic regression classifier, which can ‘learn’ to predict whether the material performed well or not based on the atomistic structure. After the training phase, the model can then evaluate more than 12,000 lithium-containing solids and find around 1,000 of them that have a better than 50% chance of exhibiting fast lithium conduction4. Progressing solid state batteries along the development path is therefore another clear use-case for artificial intelligence. Conclusion: energy storage is one of the most important considerations for the coming decades Having better energy storage solutions will help global society in myriad different ways. The classic case—there are intermittent power generation sources like solar and wind that can use batteries to equilibrate the flows of energy across time. However, I think we’d all love smartphones that don’t need a charge for a week or electric vehicle batteries with long range that can charge in similar times to what it previously took at a gas station. Sources 1 Source:Volts - A primer on lithium ion batteries 2 Source: Temple, James. “How robots and AI are helping develop better batteries.” MIT Technology Review. 27 September 2022. 3 Source: Temple, 27 September 2022. 4 Source: Reedgroup Stanford
NASDAQ:NQINTEL
by aneekaguptaWTE