Mediaboss Marketing

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

BOISE, Idaho, March 3, 2022 /PRNewswire/ -- With a historic 4.5 million workers willingly leaving their jobs in November 2021, there are more questions than ever about what's driving the change and how employers can retain talent.

EmpowerPoints, an employee engagement platform provider, revealed answers to those questions from its artificial intelligence engine. The findings showed employers can improve employee retention by:

"We know the reasons behind the Great Resignation are varied and complex," explained Brandon Poe, CEO of EmpowerPoints. "But in a series of surprising revelations, some of the best solutions to increase employee retention are not the usual go-to answers that employers gravitate towards."

Creating Momentum with a Small Percentage of EmployeesEmpowerPoints discovered that the typical "boil the ocean" approach to revamping corporate culture isn't always the right strategy. In fact, the data showed that increasing engagement with just 20% of the workforce caused other engagement factors to increase as well. Those small changes were enough to completely transform engagement within the entire organization and increase employee retention.

"Companies often feel the need to influence every employee to impact culture, but we found that's not always necessary," said Ruben Navarrete, Chief Innovation Officer at EmpowerPoints. "We're not suggesting employers only focus on a small part of the employee base, but instead proposing they consider small steps that can massively pay off."

Using AI to make informed decisions discourages attempts to make large-scale changes that are time-consuming and not necessarily as effective.

Creating Shared Meaningful Contributions Insights also revealed a contradictory concept. If a company is struggling financially or employees are personally under pressure, employee engagement and retention increase, but only if the employer provides avenues for employees to support their co-workers. "Meaningful contribution" is a commonly overlooked catalyst that increases employee retention.

"Whether supporting co-workers through tough times or collectively taking a pay reduction to prevent layoffs, employees were far more engaged when they could contribute to solutions during challenging times," explained Navarrete. "The message is to not shield employees from difficulties that the organization or employees face. Instead, create a 'we're in it together' attitude by offering opportunities for employees to become part of the solution."

Delivering Unfiltered Customer Feedback The analysis also showed that employees are more motivated to improve when candid customer comments reach them.

In one situation, each time an employee received direct feedback from a customer, the employer was instructed to monetarily reward the employee even when the feedback was negative. AI discovered this built significant loyalty between the employee and the employer, increasing retention and employee satisfaction.

"The lesson here is that employers shouldn't protect employees from the truth," said Poe. "Once you share that feedback, there are simple ways to incentivize employees and build deeper trust between the employer and employee that extends beyond monetary recognition."

About EmpowerPointsFounded in 2007, EmpowerPoints offers an array of tools and services designed to help companies understand their employees in a meaningful way. EmpowerPoints then offers the necessary systems to help companies improve their culture through real-life, easy to adopt systems, programs and solutions that drive optimal organizational culture.

Companies can learn more about the evolving EmpowerPoints platform, schedule a demo or sign up for a 60-day free trial online. For the latest news and offerings, follow EmpowerPoints on Facebook or LinkedIn.

Media Contact:Ruben Navarrete208-863-8982[emailprotected]

SOURCE EmpowerPoints

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Using Artificial Intelligence, EmpowerPoints Unlocks the Mysteries of Retention During the Great Resignation - PR Newswire

Smart Finance

Neural networks of Smart Finance will empower and optimize its financial crypto platform with unique market features

London, UK, March 03, 2022 (GLOBE NEWSWIRE) -- Smart Finance today announced its revolutionary, decentralized finance (DeFi), crypto solution. The solution is based on Artificial Intelligence (AI), Machine Learning (ML) and Mathematical Expectations (ME). Through these technologies the Smart Finance Token provides a secure, and cost effective strategy for intelligent trading on the cryptocurrency market, creating excellent value for investors.

Smart Finance, currently finalizing details of bleeding edge technical designs for release to the market, is set to become the top cryptocurrency trading tool with precision trading through computer aided decision making to maximize profit potential. The 3 computer aided technologies allow for better, more efficient, trading with optimal results and a safeguard against typical human errors.

During an interview with the Founder of Smart Finance TheZarchitect, he said Artificial Intelligence is the ultimate game changer in the crypto sector. AI provides a smart and powerful automated trading robot that can estimate crypto market values and automatically trade for you. This is the best crypto technology and the most efficient and straightforward method for trade monitoring and dedicated oversight. At any given time, Investor will know the status of their portfolio.

Invest smart, and beat the market volatility

Extreme volatility of cryptocurrency trading will always carry risks, Smart Finance offers a platform where these risks mitigated through intelligent investing. Smart Finance offers the potential for substantial rewards, better than any other methods of trading on the market today.

During market days, prices fluctuate rapidly, providing opportunities to generate consistent revenues. To maximize profits from these price movements, large volumes of data must be processed quickly. The Artificial Intelligence and Machine Learning capabilities at the heart of Smart Finance offer its users a great advantage through processing large quantities of data quickly, and accurately.

Story continues

Smart Finance brings a plethora of unique features to its clients, adding value and safeguarding the investor. The AntiScamAI (ASAI) is an AI-powered scanner, which will analyse new token projects to determine potential scams.

ASAI works on a three-step analysis:

Step 1 Website, social media and whitepaper analysis

The content of the project website is first analyzed, such as the text and images used. Additionally any white paper and related social media are checked. The AI analyzer is checking, and making a score based on the writing fluency level, any plagiarisms, paraphrasing from other projects and any images that have been copied.

Step 2 Token smart contract checking

The token smart contract for the project will be checked for red flags, typical tests used during full audits are leveraged. SMF AI check if the liquidity is locked, test the token ownership, check the maximum allowed taxes and whether a block from selling function exists. Another score is calculated.

Step 3 Broad online media search

The final step in checking the project is to search online content general for market consensus, understand what people are saying. Leveraging NLP (Natural Language Process), SMF check a defined scope of crypto-orientated news websites and discussion forums.

Invest smartly, like the whales.

Another feature is Whale AI Tracker, which can track the wallets of user selected whales. The AI engine will be able to monitor for transactions, and copy them before they complete. Utilizing this feature allows Investor to trade as the whales do, and profit when they do.

Finally, SMF have an auto-trading AI powered bot. A first in the crypto world to be powered by AI, it provides a loss insurance to the user.

A Smart Finance spokesperson explained, The main advantages of AI and ML are the ability to analyse large amounts of data, an amazing ability to learn, and benefits of taking action with accuracy and speed.

Neural networks have the power

At the center of the Smart Finance AI platform are the neural networks. These powerful networks produce forecasts around the dynamics of the cryptocurrency market. The system monitors, compares and forecasts exchange rate variations during the trading day with accuracy of up to 90%. Smart Finances approach centers around technical and fundamental analysis.

This may make things considerably simpler for new cryptocurrency traders who haven't yet had time to learn how the crypto world works. In order to determine the mood of the crypto market, an investor requires analysis of a large amount of data. This includes articles, blogs, forums and even the comments that go with them. SMF platform, which is built on artificial intelligence technologies, automatically performs the analysis and provides instantaneous, actionable results, added Neil Doody, CTO of Smart Finance.

SMF Tokenomonics

The token underneath Smart Finance also comes with its own value added tokenomics, designed to encourage and provide value to long term holders. On all buys and sells of the SMF token, a 11% tax is levied, and all existing investors are rewarded with 3% in the stable coin USDT. This is a great way to reward for long term investors, who will accrue rewards through each transaction that is made.

SMF also offer staking, Holder can stake their tokens to receive daily compounding interest. When holder can stake their tokens, will continue to receive rewards from the tax distributions in USDT.

For more information and updates, please visit:

Website: https://smartfinancetoken.com

Telegram: https://t.me/SmartFinance_SMF_AI

Twitter: https://twitter.com/SmartFinance_AI

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Smart Finance ICO Launch will be announced soon, a New Grade of DeFi Platform Based on Artificial Intelligence - Yahoo Finance

Having an A.I. assistant: The Good, The Bad, and the Ugly.

"Hey, S voice, where can I get some free food nearby?"

I don't know how many times I wanted to ask my phone these things. Part of me wants to hear an answer that goes: "Hey, you're in luck, there's a small diner right across the corner. They're giving away free burgers for single desperate lonely guys like you."

It hurts. But this burn is something that I can handle if it means that I get to have free food.

And yet, this never really happens. It's always a generic answer that goes as "Hey, here is a list of eateries nearby."

My lazy, good-for-nothing butt doesn't want this. Oh, if only we had an A.I. that was actually worth it...

Yes, you know where this is going. If you had any confusion now, I'll clear it.

Personal assistant A.I. has been all the rage, well, since as long as we have learned about the concept of A.I.

Since the advent of smartphones, the idea of a virtual Artificial Intelligent assistant has taken the world by storm.

Whether you have Apple's Siri, or Samsung's S Voice, or Cortana from Microsoft, A.I. assistants are now everywhere.

In today's article, we will be focusing on some of the great aspects of A.I. and its impact on human lives. We will also talk about what A.I. in its current form needs to improve.

We will also discuss what is the scope for improvement in all the aspects of A.I. where it is utter garbage.

So, let's get going now, shall we?

The Good:

Even though the A.I. personal Assistant isn't nearly at the level where we can compare it to the likes of J.A.R.V.I.S., it's a close second.Despite being rudimentary when compared to fictional digital butlers, current A.I. can do a lot of things.

This Includes:

It doesn't matter if you're the CEO of a 500-million-dollar company or a student. You can always trust that an A.I. personal assistant will take care of mundane low-value tasks.

Some of the best A.I. personal assistants can actually replace full-fledged assistants. Take the case for SIRI here. Apple's Magnum Opus iPhone is nothing without its operating software and voice.

Not only Siri can act as a friend and share jokes, but it can also make calls, give messages, give recommendations on the basis of web search results.

And this is just one of over a dozen Personal A.I. assistants applications. Cortana, which is from Microsoft can schedule email, create and write notes, and even schedule meetings.

Now that's something, isnt it?

There are hours upon hours of content online that is just waiting for you. All this optimization can happen via a Personal A.I. assistant. All you need to do is search for it.

This is only the best thing about having a full-fledged personal A.I. assistant. Let's see the other side of the coin.

The Bad:

No matter what we do, for now, there are certain limitations for personal A.I. Assistants.When it comes to mundane tasks, then you can rely on personal assistants, but anything more than that means you're asking for trouble.

For example,As of writing this article, your personal A.I. assistant will not be able to inform you when your apps need to be updated.

Nor will it be able to delete an app or change anything from the notification settings.Not so neat.

There are other things as well which include not being able to see your health. Unless and until you have enough dough to grab an i-watch or something, yeah, your personal A.I. assistant will not be able to inform you how many steps you have to take.

As you can see, anything that is dependent on voice commands that includes altering anything, cannot be done by these personal A.I. Assistants.

And we haven't even reached the Ugly Part yet.

The Ugly:

The saddest part, and I mean the saddest part for most people out there, is that they feel that they are substituting human emotions with an A.I.

Asking emotional questions to Artificial Intelligence at first seemed a normal thing to do.

After all, it's just for fun. But as we see in societies where A.I. becomes too mainstream, emotional connections between humans seem to be the first thing to go.

Now is this a symptom of a deeper problem or it's just a result of something different, no one really knows. But this goes to show that as humans, we still haven't been able to adapt clearly to the advance of A.I.

Conclusion:

When it comes to A.I. then the highs can be very high, and the lows can be very low.

Personal A.I. can be great for business owners, and students, especially when it comes to moderating simple tasks.

With the current advance in the field and its integration to human platforms, only time can tell where things are headed.

It's also important to remember, that the integration of A.I. right now is still in its nascent stage and there is a lot of room for growth.

We will see you at the next one.

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Artificial Intelligence Assistant: The Good, The Bad, and The Ugly. - Finextra

March 2, 2022 Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have transformed many industries and areas of science. Now, these tools are being applied to address the challenges of cancer biomarker discovery, where the analysis of vast amounts of imaging and molecular data is beyond the ability of traditional statistical analyses and tools. In aspecial issueofCancer Biomarkers, researchers propose various approaches and explore some of the unique challenges of using AI, DL, and ML to improve the accuracy and predictive power of biomarkers for cancer and other diseases.

The biomarker field is blessed with a plethora of imaging and molecular-based data, and at the same time, plagued with so much data that no one individual can comprehend it all, explained Guest Editor Karin Rodland, PhD, Pacific Northwest National Laboratory, Richland; and Oregon Health and Science University, Portland, OR, USA. AI offers a solution to that problem, and it has the potential to uncover novel interactions that more accurately reflect the biology of cancer and other diseases.

Promising applications of AI, DL, and ML presented in this issue include identifying early-stage cancers, inferring the site of the specific cancer, aiding in the assignment of appropriate therapeutic options for each patient, characterizing the tumor microenvironment, and predicting the response to immunotherapy.

A comprehensive overview of the literature regarding the use of AI approaches to identify biomarkers for ovarian and pancreatic cancer illustrates underlying principles and looks at the gaps and challenges that face the field as a whole. Ovarian and pancreatic cancers are rare, but lethal because they lack early symptoms and detection. Lead investigator Juergen A. Klenk, PhD, Biomedical Data Science Lab, Deloitte Consulting LLP, Arlington, VA, USA, and colleagues describe studies using AI and ML to analyze images for the early detection of disease, and models that can be built to predict likely outcomes for the patient. Some of the challenges, such as the difficulty of gathering large enough datasets, are discussed.

Algorithms develop biases and produce prejudiced responses when the data they are trained on are non-representative or incomplete, Dr. Klenk said. The investigators suggest that the development of larger and more diverse image databases for rare cancers across institutions, standardized reporting methods, and easier-to-understand interfaces that increase user trust are needed to make a true impact on biomarker discovery.

Lead investigator Debiao Li, PhD, Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA, and colleagues developed a model to identify individuals at risk for pancreatic ductal adenocarcinoma (PDAC). PDAC is associated with many preconditional abnormalities that can be visible on a computerized tomography (CT) scan, but these are difficult to comprehend by visual assessment. In their study, the investigators used CT scans from patients with confirmed PDAC and CT scans from the same patients who had had a CT scan six months to three years before diagnosis to identify a set of CT features that were potentially predictive of PDAC. The model was 86% accurate in classifying the patients and the healthy controls, using the identified CT features.

The challenge of AI for the advancement of pancreatic cancer research is the scarcity of data due to low prevalence. The purpose of this proof-of concept model Is to encourage researchers to establish a larger dataset for extensive training and validation of the model, said Dr. Li.

Radiomics is an emerging field where features are extracted from medical imaging using various techniques. Radiomic features can quantify tumor intensity, shape, and heterogeneity and have been applied to oncologic detection, diagnosis, therapeutic response, and prognosis. Lead investigators Shaoli Song, PhD, Shanghai Medical College and Fudan University, Shanghai, China, and Lisheng Wang, PhD, Shanghai Jiao Tong University, Shanghai, China, and colleagues combined radiomic data from preoperative positron emission tomography (PET) and CT images in patients with early stage uterine cervical squamous cell carcinoma. They used algorithms to develop a prognostic signature capable of predicting disease-free survival.

This model could provide more accurate information about potential relapse and metastasis, and could be helpful in decision-making, they observed.

Other papers in the special issue focus on the development of new computational tools to facilitate the application of AI to biomarker identification; the use of whole cell imaging and immunofluorescence to identify immune features in pancreatic tumors to provide prognostic information; the use of microRNAs and applied machine learning to identify a miRNA profile associated with gastrointestinal stromal tumors; and the use of hierarchical clustering of combined multi-omic datasets to identify an antitumor immune signature in patients with colon cancer.

Dr. Rodland added that the articles in this special issue are only a small sampling of the various approaches to using AI, DL, and ML in biomarker research. There is a continuing urgent need for more effective strategies for improving the early detection of cancers. Cutting-edge AI systems have been shown to improve sensitivity and specificity in the interpretation of both imaging and non-imaging data for breast, lung, prostate, and cervical cancers, she stated.

For more information: http://www.iospresscom

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Artificial Intelligence and Machine Learning Show Promise in Cancer Diagnosis and Treatment - Imaging Technology News