It has been five years since ISS Art agency started focusing on the development and integration of AI software solutions. Now is a good time to look back and think about whether this trend of custom software development is still relevant, or should we move into something like web3 or Going back to the classic web/mobile development focus.

To do this, let’s first think about why — why did we focus on AI in the first place? Because for us as a custom software development agency, it’s a great opportunity to get into a new business or even a new line of business.

A long time ago, software development solved the problem of automating business processes and any other business or production activity. AI lets you do the same thing, but at a new level of quality. Thus we can significantly improve and rework the classically solved task Software in a wide range of business domains.

In fact, it is hard to imagine how wide a range of tasks can be solved with the help of artificial intelligence.

Below we’ve compiled a list of the most unexpected and specific tasks that could be solved using artificial intelligence. We’ve come across some of these at work, but some took us by surprise.

Whiskey brewing 

It may not seem so at first glance, but artificial intelligence and the whiskey industry are a perfect match.

The distillery fed recipe sales data and customer preferences into a machine-learning model, which highlighted the most popular and highest-quality blend results—the AI ​​whiskeys.

It’s a fresh take on an old classic. Of course, some might argue that it’s inevitable that AI will eventually develop enough to make beverages. But how does it work, and what can you expect from drinking this futuristic drink?

AI Whiskey is the product of one man’s desire to create an alternative method for distilleries to make whiskey and save them money at the same time. Alexandre Gabriel, founder of Pernod Ricard, hopes it will be a lower-cost process that will help give smaller companies more resources in the long run.

The process is simple and involves using still images transformed by AI technology. The system then blends the whiskey through its own steps – mashing, fermentation, distillation and maturation.

Kentucky-based liquor maker Beam Suntory is one company using artificial intelligence to innovate the process of whiskey production, using an algorithm they developed called Beam Complete. The system predicts how flavors will mix with each other and in A batch of product leaves the bottle.

The whiskey industry is a perfect candidate for AI as it relies on data to provide feedback insights and recommendations to perfect the perfect blend.

Thankfully, this can be done without any human input—all you need are the right algorithms and robots that know how to extract meaning from mountains of data. The sudden increase in demand shows no signs of slowing down as interest in craft breweries grows. and Artificial intelligence is entering the industry, and we can only expect more good things to come.

AI Death prediction 

Using socioeconomic and clinical data, researchers developed an artificial intelligence tool to predict the 30-day risk of death in cancer patients.

Cancer is one of the leading causes of death in the United States each year. Using artificial intelligence tools, medical professionals can identify high-risk patients and provide early intervention and solutions for reversible complications.

Additionally, the tool can identify those patients approaching the end of life (EoL) and refer them to early palliative and hospice care. Doing so will improve patients’ quality of life and symptom management, according to research.

Physicians often postpone advance care planning and EoL conversations until patients are near the end of life. Current methods and models for predicting mortality vary in accuracy, include only clinical factors and provide no additional information to clinicians.

However, the authors observed that including sociodemographic and geographic factors in AI predictive analytics models could identify patients at higher risk of short-term mortality, leading to better management and care for cancer patients.

The researchers conducted this study by selecting 3671 patients from a de-identified database representative of a large community hematology/oncology practice. Data from de-identified databases include electronic health record (EHR) billing data and socioeconomic determinants of care.

Data collected from patients includes demographic data (e.g. gender race age) and cancer information (e.g. type stage). In addition, the researchers looked at socioeconomic data, including lifestyle choices and individuals’ living circumstances. Socioeconomic data are in analyze.

A machine learning algorithm was able to accurately predict 30-day mortality in cancer patients. The study also examined the algorithm’s ability to predict 60-90-day and 180-day mortality, with similar results.

The researchers concluded that the machine learning algorithm’s ability to identify cancer patients at risk of 30-day death has the potential to improve outcomes for patients with reversible clinical factors. In addition, AI systems can prevent attacks on those Approaching EoL.

Generating whole ADS videos 

Phyron, a video creative automation platform, pulls data from its brand customers—dealers or automakers. Use Phyron software to make at least four images from the car for sale.

AI identifies key attributes in car images and automatically edits them into video templates. It can identify the differences between interior and exterior photos, and it can also remove backgrounds and replace them with neutral ones. The software is also Professional appearance along with logo and other branding assets.

Product details pricing and special offers are also designed into the video, and the video is re-rendered if the brand makes any changes to the details in the data feed.

Competitors are raising the bar in content quality and production competition. As a result, static images that can be more easily fetched from websites to social media marketing promotions no longer work for big brands or even local car dealerships. This adds support for video and Democratize video content production with automated solutions as well as copywriting for written messages. There is a similar push to standardize and simplify the production of 3D images.

Phyron CEO Johan Sundstrand sees similar expectations for car marketing in Europe and North America. They use similar performance metrics (inventory to incremental views, lead generation cost per video) that derive a similar value proposition from the Phyron technology. dealer wants Employees spend the least amount of time updating lists and more time with customers.

Fraud detection in finance  and other domains 

As online shopping increases, fraudulent transactions are also on the rise. Companies use machine learning algorithms to identify and stop fraudsters. You’re watching Game of Thrones when you get a call from your bank asking if you paid “$X” with your card Shop for gadgets at stores in your city. It’s not you who uses your card to buy expensive equipment; it’s in your pocket all day long. How does the bank flag this purchase as fraudulent? Thanks to machine learning algorithms. Financial fraud costs $80 billion a year, with only Americans in $50 billion worth of risk per year.

One of the most important goals of applying machine learning in banking/finance is fraud prevention, i.e. detecting and minimizing any fraudulent activity. Machine learning is best suited for this use case as it can scan large volumes of transactional data and identify mode, i.e. whether there is any unusual behavior. Every transaction made by a customer is analyzed in real time and given a fraud score representing the likelihood of the transaction being fraudulent. In the event of a fraudulent transaction, the transaction will be blocked or handed over to human review. all This happens in the blink of an eye. If the fraud score is above a certain range, a rejection is automatically triggered. Otherwise, without applying machine learning, it would be difficult for a human to look at 1,000 data points and make a decision in seconds.

Citibank has partnered with Feedzai, a Portugal-based fraud detection company that alerts customers in real time to identify and eliminate fraud in online and in-person banking. PayPal is using machine learning to fight money laundering. PayPal has several machine learning tools Compare billions of transactions and accurately distinguish between legitimate and fraudulent transactions between buyers and sellers.

It should end with the fact that the direction of artificial intelligence is clearly not outdated.

Today, we can outline the top five business areas where machine learning is applied in the most successful way:

• Fraud detection;
• Virtual personal assistants;
• Product recommendations;
• Speech recognition;
• Customer segmentation.