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LAS VEGAS--(BUSINESS WIRE)--Tachyum was highlighted as one of the Most Innovative Artificial Intelligence Solutions Providers to Watch by The Enterprise World and earned the featured cover story with an in-depth discussion about the company, its leadership, vision of the industry and the AI attributes of its Prodigy universal processor.

The Enterprise World, with its wide topics for every month, brings to readers new and changing trends in business, market growth, changing government reforms and the growing customer base of a particular industry. As part of each issue, the magazine features the success stories of people from the enterprise world with exclusive interviews that will help readers learn different and efficient ways to run their businesses. Tachyum is featured in the latest issue with an in-depth profile of the company, its founder and CEO Dr. Radoslav Danilak and how Prodigy is going to impact the future of AI.

As AI migrates to more sophisticated and control-intensive disciplines, such as Spiking Neural Nets, Explainable AI, Symbolic AI and Bio AI, Prodigy will deliver an order of magnitude better performance than its competitors, said Danilak. Prodigy-powered universal servers in hyperscale data centers, during off-peak hours, will deliver 10x more AI Neural Network training/inference resources than currently available and since the Prodigy-powered universal computing servers are already bought & paid for, the expense of operating such systems will be extremely low. I look forward to sharing details of our success in AI with readers of The Enterprise World.

Tachyums Prodigy processor can run HPC applications, convolutional AI, explainable AI, general AI, bio AI, and spiking neural networks, plus normal data center workloads, on a single homogeneous processor platform, using existing standard programming models. Without Prodigy, hyperscale data centers must use a combination of disparate CPU, GPU and TPU hardware, for these different workloads, creating inefficiency, expense, and the complexity of separate supply and maintenance infrastructures. Using specific hardware dedicated to each type of workload (e.g. data center, AI, HPC), results in underutilization of hardware resources, and more challenging programming, support, and maintenance. Prodigys ability to seamlessly switch among these various workloads dramatically changes the competitive landscape and the economics of data centers.

The article is available in the February 2022 issue of The Enterprise World.

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About Tachyum

Tachyum is transforming AI, HPC, public and private cloud data center markets with Prodigy, the worlds first Universal Processor that delivers industry-leading performance, cost, and power efficiency for both specialty and general-purpose computing. When Prodigy processors are provisioned in a hyperscale data center, they enable all AI, HPC, and general-purpose applications to run on one hardware infrastructure, saving companies billions of dollars per year. A fully functional Prodigy emulation system is currently available to select customers and partners for early testing and software development. With data centers currently consuming over 3% of the planets electricity, predicted to be 10% by 2025, the ultra-low power Prodigy Universal Processor is critical if we want to continue doubling worldwide data center capacity every four years. Tachyum, Co-founded by Dr. Radoslav Danilak with its flagship product Prodigy, is marching towards tape out targeting Q2 2022, with software emulations and an FPGA-based emulator running native Linux available to early adopters. The company is building the worlds fastest 64 AI exaflops supercomputer in 2022 in the EU with Prodigy chips. Tachyum has offices in the United States and Slovakia. For more information, visit https://www.tachyum.com/.

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Tachyum Named Among the Most Innovative Artificial Intelligence Solutions Providers to Watch by The Enterprise World - Business Wire

Today, there are more and more vehicles on our roads, which creates an increased risk for accidents and associated injuries and deaths. Globally, about 1.3 million people die each year as a result of traffic crashes.

With artificial-intelligence technology paving pathways for many industries, transportation is beginning to utilize the benefits of AI to increase driver safety, driving and overall road safety.According to market research, the global artificial intelligence market size was valued at $51.08 billion in 2020 and is projected to reach $641.30 billion by 2028. Manufacturers have identified multiple ways AI can be utilized on the roads and ultimately provide an increase in overall safety and comfort for drivers and passengers.

Primary Use Cases for AI in Transportation

From cabin safety to road conditions and urban planning, AIs ability to detect and record patterns can provide drivers with information needed to make accurate decisions.

Primary use cases include:

Traffic Management

AI in transportation utilizes closed-circuit TV cameras and sensors that can record valuable insights while on the roads to pick up on traffic conditions and prepare drivers for delays on the way. This data is stored through cloud AI or Edge AI systems that create a quicker way to store traffic pattern recognition to predict the status of roadways.

Edge AI technology is a method of faster computing and can enhance the overall performance of applications based on AI and increase the accuracy with its deep learning capabilities.

Fleet Management

Logistics companies have begun to use AI to keep up with the increasing delivery demands across the world. Companies such as Amazon require 999 out of 1,000 deliveries to be made on time, which puts lots of pressure on drivers. AI-based technology plays a large factor in keeping drivers safe in high-stress environments, as well as making route corrections for efficiency.

Researchers predict by 2023, the global transport AI market will reach $3.5 billion. The implementation of AI in logistics companies allows for the measurement of driver behavior and performance, assisting with human decision making, fleet visibility, predictive repair and maintenance, and predicting the most fuel-efficient routes.

Increasing Public Safety

AI not only can detect objects, but also can differentiate between inanimate objects and people. The technology even has been useful in detecting pedestrian traffic. In major cities, pedestrian crossings can create a large potential for accidents.

Dashcams help allow the driver to see all sides of the vehicle as well as sensors detecting any objects around the vehicle. Todays AI-based dashcams and sensors combine integrated technology to detect pedestrian walkways a few miles ahead to equip drivers with the best knowledge possible for navigating pedestrian-heavy cities.

The Future of AI

AI is transforming the automotive industry in more ways than one.

More innovations are being created all the time with vehicle-to-vehicle connectivity being at the forefront, which allows for the sharing of vehicle information such as speed, location and any hazards on the road. Overall, AI is prioritizing safety and helping drivers manage busy roadways and be aware of hazards.

Claude Hochreutiner (pictured, above left)is director-Platform & Data Managementfor Smarter AI.

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Artificial Intelligence Doing More to Increase Driver Safety - Ward's Auto

TORTOLA, BRITISH VIRGIN ISLANDS / ACCESSWIRE / March 4, 2022 / Galaxy Trading Analytics (GTA), headquartered in the British Virgin Islands (BVI), is a technology company specializing in world-class artificial intelligence. GTA is empowering cryptocurrency investors and traders with a novel approach to growing their portfolios amidst the Covid-19 pandemic.

Initially developed for private clients and institutions, they are now offering their GTAI (Galaxy Trading Artificial Intelligence) System to the masses, which have developed a reputation in the industry with consistent trading profits.

GTAI System, the World's First Hybrid AI Trading and Arbitrage Software Bot, is designed to help crypto traders maximize trading profits while minimizing risks and losses.

Arbitrage has been a strategy utilized by investors and traders in growing their portfolios by leveraging price asymmetries and inefficiencies across different exchanges or markets. As a method of trading, it requires a high level of expertise, experience, and involvement, thus making it inaccessible for most.

Technology is changing this, by providing platforms that make it accessible to traders by utilizing artificial intelligence, algorithmic trading, and lightning-fast transactions in successfully executing arbitrage for portfolio growth.

Unlike traditional arbitrage bots that only deploy triangular arbitrage, GTAI deploys 4 different trading strategies, making it more stable and profitable even in bull, bear or volatile market conditions. In the future, GTA will even implement more proven trading strategies into the GTAI System.

The GTAI system is monitored 24 hours a day, 7 days a week by a dedicated team, deploying the right strategies and risk management protocols according to the market conditions.

About Galaxy Trading Analytics

Galaxy Trading Analytics, GTA, is a British Virgin Islands based regulated fintech company established in 2022, with an office in Canada, and teams operating around the world. With a strong team of Artificial Intelligence and Deep Learning experts since 2013, their core focus is to develop niche investment solutions and investment advisory tools. GTA manage and maximize their clients' assets via their AI technologies, GTAI system and a user-friendly mobile App, giving them the best yield in the crypto markets with minimal risks.

Media DetailsMike Peterson media@gtatrade.comTortola, British Virgin Islands gtatrade.com

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SOURCE: GTA Trade

View source version on accesswire.com: https://www.accesswire.com/691560/Artificial-Intelligence-by-Galaxy-Trading-Analytics-Enables-Greater-Access-to-Portfolio-Growth-and-Diversification

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Artificial Intelligence by Galaxy Trading Analytics Enables Greater Access to Portfolio Growth and Diversification - Yahoo Finance

Artificial intelligence (AI) is taking the technology industry by storm. We see a surge in solutions embedded with virtual assistants and chatbots, with large enterprises integrating AI across the entire tech stack. A recent report suggests that the global AI market will have a valuation of $190.61 billion by 2025, and the forecasted annual growth rate will be around 33.2%.

Artificial intelligence and related technologies are making our existing solutions even more intelligent and are helping us unlock the power of data. The machine learning algorithm, computer vision, natural language processing, and deep learning are now easy to bake into any solution or platform.

Artificial Intelligence can disrupt critical business processes like collaboration, control, reporting, scheduling, and more. In this blog, we will discuss ways for organizations to implement AI efficiently and effectively.

Research and Understand

First and foremost, get acquainted with what enterprise AI can do for your business. In addition to consulting with pure-play AI companies who can advise you on how best to go about this, you can also refer wealth of online information available to familiarize yourself. Some universities like Standford have online papers and videos on AI techniques, principles, etc. Your tech team can check out Microsofts open-source Cognitive Toolkit, Googles open-source TensorFlow software library, AI Resources, the Association for the Advancement of Artificial Intelligence (AAAI)s Resources, MonkeyLearns Gentle Guide to Machine Learning, and other paid and free resources available. More research gives you a head start, and you will know what you are getting into as an organization, how to plan for it, and what to expect at the end of it,

Pin-point the use case

Once you know what AI can do, the next step is to identify what you want AI to do for your business. Think of how to add AI capabilities to your products or services. Build specific use cases in mind around how AI can solve some of your challenges and add value to your business. For instance, if you review your existing tech program and its challenges, you should have a strong case around how image recognition, ML, or others can fit into the product and how useful it will be.

Attribute financial value

Once you have those use cases ready, assess the potential business impact of those and project the financial value of the AI implementations identified. Tying business value to AI initiatives will ensure you are not lost in details and always put outcomes at the center. The second part is to prioritize AI initiatives. Put all your initiatives in a 2X2 matrix of business potential and complexity, and that will give you a clear picture of which ones to go after first.

Identify skill gaps

Once you have prioritized your AI initiatives, its time to check if there are enough ingredients in the kitchen. Its one thing to be wanting to accomplish something and the other to have an organizational capability for it. Before launching a full-blown AI implementation, you can assess your internal capacity, identify skill gaps and then decide on a course of action. You may hire additional resources, or you can tie up with pure-play product engineering companies specializing in AI.

Pilot under the guidance of SMEs

Once you are ready as a business, start building and integrating AI within the business stack. Have a project mindset, and importantly ensure that you dont lose sight of business goals. You can consult with Subject Matter Experts in the space or external AI consultants to ensure that you are on track. Your pilot will give you a taste of what long-term implementation of an AI solution will involve. The pilot will make the case even strong, and you can decide if it still makes sense for your business. But for the pilot to succeed, you will need a team of your people and people who know AI to keep it impartial. Having external SMEs or consulting partners is a great value add at this stage.

Massage your data

High-quality data is the basis of a successful AI/ML implementation. It is critical to clean, massage, and process your data to get better results. Usually, data for enterprises is in multiple silos and various systems. Form a small unit, especially cross-functional, to integrate different data sets, resolve inconsistencies, and ensure that the output is high-quality data.

Take baby steps

When you start, start small. Apply AI to a small data set to test thoroughly. Then incrementally, you can increase volume and collect feedback continuously.

Plan for Storage

Once your small data set is up and running, you need to start thinking about additional storage to implement the full-blown solution with complete data input. The algorithms performance is equally important as its accuracy. To manage large volumes of data for better accuracy, you need a high-performing solution supported by fast and optimized storage.

Manage the Change

AI provides better insights as well as automation. But its a big change for employees as it expects them to operate differently. Some employees are warier than others, and they must accept the change positively. You will need a formal change management initiative to introduce the new AI solution augmenting their daily tasks.

Build Securely and Optimally

Usually, companies start building AI solutions around specific aspects or challenges without studying the limitations or solution requirements as a whole. It will result in sub-optimal or dysfunctional solutions and sometimes insecure too. You will need a balance of storage, the graphics processing unit (GPU), and the network to achieve an optimum. Security is also mostly overlooked, and most companies realize that post-implementation. Make sure you have security safeguards in place like data encryption, VPNs, anti-malware, etc.

AI implementation is no cakewalk, and challenges may arise at every step. But with every technology, the challenges associated with the adoption are the most difficult to tackle. Data literacy and trust are the two pillars of introducing any new technology. Another important aspect of AI initiatives is that it matures with your data management strategy. You will need both of them to run in parallel for success.

Views expressed above are the author's own.

END OF ARTICLE

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10 steps to implement artificial intelligence effectively in your business - Times of India

Wading through crop fields searching for insects, diseases, weeds, nutrient deficiencies, uneven emergence, and other maladies consumes time and effort.

Even when the corn is just knee high, you can only see a couple hundred yards in each direction, points out J.D. Bethel, an agronomist with Integrated Ag Services (IAS), Milford Center, Ohio.

This makes it almost impossible to see emerging weeds like giant ragweed that quickly become, well, giant. IAS aims to nix this scenario and others by pairing artificial intelligence developed by Taranis with flights of unmanned aerial vehicles (UAVs) during the growing season.

Taranis officials say its AI2 SmartScout captures 0.3 millimeter per pixel resolution from UAVs at a speed of 100 acres in six minutes. In comparison, the best satellite resolution is about 1.2 meters per pixel, says Mike DiPaola, Taranis general manager of North America and vice president of global sales.

It can easily identify a bean leaf beetle or a Japanese beetle on a soybean leaf, says Bethel. We have even been able to count the hairs on a soybean leaf or the colors of the flowers on soybeans. Thats the sort of resolution it can achieve.

Confirmation still is required, of course. You still want to go out and check if it is indeed a waterhemp plant that the program has identified, says Bethel.

Even when making a field visit, this technology boosts scouting efficiency, according to IAS and Taranis officials. Taranis software also contains a feature that farmers and agronomists can use to prioritize field visits.

There may be only 15% of fields that they [agronomists and consultants] need to immediately visit, says Evan Delk, IAS vice president of sales and marketing. If theyre instead trying to get across every single acre, their time is not being utilized as it should be. Our consultants need to be in front of the grower, helping them make better decisions.

Gil Gullickson

Josh Guy with Integrated Ag Services readies an unmanned aerial vehicle for flight.

Keying all this is artificial intelligence (AI) developed by Taranis. Its image bank contains more than 50 million submillimeter high-resolution images of crop disease, insects, weeds, nutrient deficiencies, and other issues compiled by more than 100 agronomists. Through its AI engine, Taranis leverages machine learning and computer vision to help farmers and consultants identify field maladies.

For example, we will take pictures of a Japanese beetle and run them however long it takes for the AI to see a pattern, says Ofir Schlam, CEO and cofounder of Taranis.

Once the computer records the pest or malady, though, it remembers it.

Whats great about artificial intelligence is it doesnt think like us, says Josh Guy, IAS operations manager. It may detect soybean diseases in a field that human eyes may not see.

Integrated Ag Services

Imagery captured by unmanned aerial vehicles (UAVs) enables maps to be made that monitor crop emergence or emerging weeds deep in the canopy.

IAS offers early-season and late-season scouting packages for $9.75 per acre, while a full-season package costs $13.50 per acre. A full-season package is the best way for farmers to monitor their fields, says Delk. In the full-season package, IAS flies UAVs across fields about every 14 days, depending on weather and crop growth progression. This service also provides an aerial overview video of each field.

Whether youre scouting on foot or with a drone, you have no idea what is happening after you leave the field, says Bethel. There can be weeds coming up, plants dying from disease, or plants still emerging. Using the IPM [Integrated Pest Management] approach, we scout every two weeks with a drone looking for weeds that may influence changes to the existing herbicide program.

The UAV and AI combination can help a farmer decide whether or not to apply a fungicide, while nutrient scouting can influence whether to apply late-season nitrogen, says Delk.

The idea is to constantly have eyes on the field, he adds. Cost savings from making or forgoing a chemical application or late-season nitrogen pass or seed savings from a selective replant (see Easier Replant Decisions) can quickly surpass the $9.75 to $13.50 per acre cost, he adds.

Gil Gullickson

Umanned aerial vehicles can provide images that enable farmers to quickly make decisions.

Some 12 to 24 hours normally pass between a drone flight and the time maps are digitally delivered to a farmers desktop computer or mobile device, says Guy.

We plan flights in advance so once we get out to a field and set up, its as simple as hitting play on the flight plan, he points out. We still need to keep eyes and hands on the controller, but it is basically a preplanned flight with the UAV.

Challenges exist. One of the major impediments in getting good imagery is wind speeds, says Guy. Technically, this equipment can fly in winds up to 25 mph. Once you get past 10 to 15 mph though, the crop moves just enough for the camera to pick up that motion and blur the image.

So much information is collected that it can be overwhelming for the farmer.

The important part of getting the value out of the data is to make sure you have a trusted adviser to make sense out of it, says Delk.

Farmers are busy, adds Bethel. We can text a farmer with a report that says, These four fields look good, but you really need to look at field five. This is a huge time savings for them. They can better allocate the amount of time they do have for more important tasks.

Replanting is where unmanned aerial vehicles (UAVs) teamed with artificial intelligence particularly shine, says Evan Delk, vice president of sales and marketing for Integrated Ag Services (IAS).

Before, we went out in the field to do five plant stand counts in a 100-acre field, he says. Now, we take high-resolution images [with the UAV] every one-half acre, which creates many data points that the farmer can use to decide whether to replant.

It takes a lot of the emotion out of the replant decision, adds J.D. Bethel, IAS agronomist. Instead of driving back and forth through the whole field wondering where they need to plant, the map shows them the worst parts of the field where they need to replant.

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UAVs team with artificial intelligence to boost crop scouting efficiency - Successful Farming