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Artificial_intelligence

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
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
BAT - CRYPTO MOVING NICELY TO THE PATTERNSBINANCE:BATUSDT has had a nice move down from the larger Patterns on Sept 5th now we are looking for more weakness down toward 0.30 to complete this ABCD pattern. If we look at todays AI there is a CIT Window at 13:00 this afternoon. If we could get one more rally up towards 0.33-0.3350 that would set up a sell signal for a minimum move back down to 0.3200. A nice one to keep an eye on as its moving nicely to the patterns I trade. I hope this helps. Enjoy the day. 👍👍
Short
by L_F
SP500 - Intraday turn points.I have marked out the Intraday turn points fot the FOREXCOM:SPXUSD . Ideal Scenario would see a rally into 4030 @ 00:30 just over half way through the US Session setting up another Sell signal. The other option for this is that it could be a low and mark a reversal. The other Key Time is @ 21:30 this might set in motion the next move higher. This is a time based strategy that can be a low or a high. Lets see hoe this plays out.. This doesnt always work but when you use patterns with it, it can give you an edge.
FOREXCOM:SPX500Short
by L_F
GBPNZD - AI & MAJOR LEVEL COMING UPToday Im taking a look at the AI for the next 2 days and how its timing in with major pattern completion levels between 1.8947 - 1.8962. Patterns completing are a Gartley from the 15th low. An ABCD from the 18th high @ 1.8962 A 3 drive completing at 1.8947 Add in the AI @ 2:30 tomorrow morning and this gives us a great set up to buy. If its at a high coming into the key time then its likely to be an invert and heading lower. Things lining up nicely. Lets see how it plays out. Enjoy the day. 👍👍👍
Long
by L_F
ETHEREUM - 2000 or 700?COINBASE:ETHUSD seems to be in the final stages of this correction. It mightve already topped out if we look at the Monthly AI or there could be one more high up towards 2000 to complete multiple patterns and the 382 from the April 3 High. Weekly AI has a High/Low late on the 9th of August. Keep an eye out for this to play out. If we are at a high then we will look for short opportunities and if its a low we will look for longs. A different look at the market. I hope this helps. Enjoy the week. 👍👍
COINBASE:ETHUSD
by L_F
1
BITCOIN - Possible Top Saturday??COINBASE:BTCUSD is still edging higher with now real confiction. Im thinking we are coming to the end of this rally. If we take a look at the AI for this week then we could see this have one more rally into Sat evening or has the high already been made just shy of 25k? There are a lot of unfinished patterns just above 26k with multiple ABCDs and the 61.8% level from the May 31 high @ 26700. Some things to watch over the coming days. Enjoy the day. 👍👍
COINBASE:BTCUSDShort
by L_F
1
BITCOIN - Key Times to watch today!!COINBASE:BTCUSD Here I take an Intra-day look at possible turn points. The early part of today is working nicely, now Im looking for an ABCD pattern up into the 50-61.8% area around 23800 @ either 16:30 or 19:30. A rally into these times would give us an opportunity to look for a short term sell. The Invert option would see the market sell off from here down towards 22300 then it would become a buy signal. Lets keep an eye on this one and see what happens. I will post updates as we get closer to the times. 👍👍
COINBASE:BTCUSDShort
by L_F
1