machine learning

Machine Learning Cases in Business

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Artificial intelligence today is not just a joke about the enslavement of humanity. Thousands of businesses are moving to AI at an incredible speed, bringing production turnover to a new level and making our lives much more comfortable. But how can AI scientists teach the algorithm not just to produce a ready-made emulsion but to adapt to changing circumstances? The principles of machine learning have come to the rescue. Check this explanation of ML basics by an expert team, and we’ll move to the practical cases which has helped many companies.

Case 1: CompanionPro

Source:cnet.com

As you know, obedient dogs are very likely to find their owners. Shelters are well aware of this, but few find the funds to pay for a large number of trainers. Michael Wang, director of the company, Companion, looking at the problem from the owner of the two cats, realized that this frightening omission requires an immediate solution. CompanionPro is an adaptive algorithm based on Tensor Flow, which can recognize the behavior of animals and reward them in the case of the successful command. Before the commercial release, the algorithm went through a humourous database of shelters, where it learned to determine what condition the dog is now, in “standing” or ” seated” as well as to select the voice timbre that will be the most appropriate. The cherry on the cake was the reward system, which empirically determined the amount of “goodies” for your favorite pet.

Case 2: Vitek “EYESYSTEM 1.0

I think everyone has ever had the urge to drink a cold yogurt in the morning. Workers in their production are no exception. How good that Vitek has developed an automated visual control system that has helped in such a difficult situation.  The yogurt goes through two major stages, such as: checking the presence of the lid and the permissible level of the product in the bottle. Previously, the first stage was extremely uneconomical in time, but it was not immune from human error. Now the algorithm detected the loss with the highest accuracy, informing the entire plant about it. After analyzing a thousand lids, he knew each one by sight. But how would he know that I had not been top-up? Yogurt is yogurt, little or much of it. EYESYSTEM 1.0 could argue with you because looking at this damn bottle for the hundredth time, even you would be able to determine the compact differences in volume, right?

Case 3: 3D fitting and analysis of Wildberries

Source:research.hktdc.com

Imagine you come in after an exhausting day at work, and there are three dirty shirts in the wardrobe, one of which has a large brown stain from an invigorating coffee drink. The chief wants to see you at the parade tomorrow as if to spite you. What will you do? Run to the store? What a pity, you have an urgent task at work. Can you order a charming costume online? But how do you know your size when you last went to the store six months ago? If you at least slightly recognized yourself in this situation, then the 3D fitting from Wildberries will appeal to you. You need to point the camera at the desired part of the body, and a clever algorithm will adjust to you. What kind of magic is this? Not magic, but the thousands of bodies that the AI had painstakingly processed to produce such an impressive result. Many of you may say: “Well, 3D fitting is certainly good, but I’m not so lazy that I haven’t walked to the store yet. I’d rather buy cheaper clothes”. Great! And for this task, an all-powerful AI would do just fine. Just take a picture of your favorite thing, and you will see hundreds of analogs that will not be inferior to the original.

Case 4: Holos Servorobot

How many articles have been written, how many scientists have talked about the importance of careful treatment of the world around us, but people continue to harm themselves. If a person can’t reason with himself, then let a robot do it! The company Holos seriously took up this issue and presented a machine that is able not only to anticipate trouble but also to deliver dangerous cargo. Servorobot is equipped with a variety of sensors that can analyze the environment with high accuracy, determining the danger. Especially important in oil fields, where an unfortunate gas leak can not only leave you without cheap gasoline but also flatten the nearest neighborhood. In addition, this big guy is educated by the bitter experience of gigabytes of data, so if there is a war, your cargo will be safe and sound.

Case 5: SAT Logistics Optimization

Source:blessgroup.net

Have you ever wondered how your new phone moves from the store to you? Of course, you know, there is a conditional point A, where you start the path, and point B, where it ends. A simple math problem, but why do you wait so long, sometimes even overpaying for delivery? The answer will be found if you look at this process through the eyes of the logistics department. Your parcel can generally make eight circles around the city until the courier still knocks on your door. A logical question would be: “Can this be avoided?» Can. This is done by logisticians, who, however, do not always perform their work in good faith. So it turns out that there is corruption and deception everywhere? Corruption is not quite, but the deception is successfully fought by SAT, which has implemented logistics optimization using geolocation. The Google Maps API, which conveniently allowed them to mark the place where the goods will be delivered on the map even without knowing the address helped this. Their architecture was able to analyze the routes of the company and choose the optimal chain of delivery of goods. Moreover, the cunning logisticians could no longer leave the best for their friends, because now big brother was watching them.

Case 6: Ecovacs Robotics Deebot

Source:amazon.com

Perhaps I won’t surprise anyone if I say that vacuum cleaners have learned to clean your house themselves, but it’s also not surprising to hear that their intelligence wants the best. What annoyed you the most? Oh, yes, I know what you’re thinking. This “beautiful” moment, when this silly boy once again gets entangled in a neatly lying wire, gives the impression that he is your restless pet, and not the crown of technological progress. The head of the AI department of Ecovacs Robotics has faced this problem more than once since he decided to teach the robot to recognize the result of your sloppiness. Using a huge image data set, the robot learned to analyze small objects and masterfully drive around them. Now such insidious things as discarded socks, a black wire, or a red lego cube are left behind, saving you another nerve cell.

Conclusion:

To sum up, I would like to say that this is not all that machine learning is capable of business. Many areas need the development of this technology more than ever. It can turn a loss-making enterprise into a gold mine, and in principle change the concept of business. The main thing is not to be a conservative and think again: “Maybe a call center of fifty people is not the best idea for a pizzeria?”

Top 8 Technology Trends and Jobs 2021 Will Bring

Technology is developing at staggering speeds, and it is only getting faster. This evolution enables faster change and progress. Careers in tech industry do not follow as fast, but they do evolve. IT professionals tend to recognize their new or expanded roles, as they constantly learn because they both want and need. Everyone should always stay in touch with the modern technology trends today. You need to set your eyes on the future and realize which skills you will need for jobs you want to do.Let us explore eight technology trends for the year 2021, as well as the jobs they will create.

Img source: evolving-science.com

8. Artificial Intelligence (AI)

Artificial Intelligence has undoubtedly already received great attention and continues to be a trend to watch. It more and more effects how we live, work and play, and yet it is only in the early stages of development. Other AI branches also appeared as well. AI are computer systems that mimic human intelligence. They perform tasks like recognizing images, patterns or speech, and they also make. AI can actually do these tasks much faster and more accurately than humans can.

Img source: medium.com

7. Machine Learning

Machine Learning is one of the newer branches of AI. Here, computers are programmed in a way to learn how to do something they are not programmed to do. This basically means that they learn more by discovering patterns and exploring data. There are supervised and unsupervised learning, while Machine Learning also has subsets that include neural networks, natural language processing (NLP), and deep learning. Each offers different opportunities for specializing in career fields that constantly grow and improve.

Img source: medium.com

6. Robotic Process Automation (RPA)

Process Automation is another technology developed to automate jobs. Essentially, it is the use of software to automate business processes like processing transactions, interpreting applications, dealing with data, and replying to emails. RPA serves to automate repetitive tasks people used to do, to give them more time to do more important work. However, not just menial tasks are automated, as 45 percent of activities we do could potentially be automated, like the positions of financial managers, CEOs and doctors.

Img source: medium.com

5. Blockchain

Here, most people will think in relation to crypto currencies like Bitcoin. Blockchain however offers useful security in different ways. It is the data you can only add to, but not change in any other way.The fact that you cannot change it is why it is so secured.These are also consensus-driven so that nothing else can control the data. With blockchain, you do not need a third party to validate or even oversee any of your transactions.

Img source: youtube.com

4. Edge Computing

Cloud computing is currently mainstream, with giants like Amazon Web Services, Google Cloud and Microsoft Azure dominating the market today. Cloud computing and its adoption are still growing, with more and more businesses moving to their solutions. It is no longer an emerging technology, because Edge is. It is practically designed to bypass latency that cloud computing causes and get data to a data center for processing. In other words, it exists “on the edge,” or closer to where the computing needs to be. Edge can process time-sensitive data in very remote locations with little to no connection to one centralized location.

Img source: justscience.in

3. Virtual Reality (VR) and Augmented Reality (AR)

VR immerses the user in a virtual environment, while AR enhances the actual environment. VR has is at the moment mostly used for gaming and training. The popular Pokemon Go game is a great example of AR. Both have huge unexplored potential in education, training, entertainment, marketing, and perhaps even rehabilitation. They both could be used to train doctors, offer museum tours and experiences, enhance the theme parks and marketing, etc. The potential is next to limitless here.

Img source: rackspace.com

2. Cyber Security

Cyber security is around for a while, and is evolving just as any other technology. There are always new threats, so security has to keep up. The hackers want to access all sorts of data illegally, and they will not stop, but continue to develop their skills to bypass the toughest security measures. Most new technology comes with enhanced security, for example hardware authentication, cloud technology and deep learning. Data loss prevention and behavioral analytics are also great ways of security in the cyber world. As long as hackers are active, cyber security will evolve and develop to defend us against them.

Img source: itpro.co.uk

1. Internet of Things

Internet of Things (IoT) is the future. A lot of “things” are have WiFi connection, meaning that they could easily be connected to the Internet, as well as to each other. This is what IoT stands for. It enables cars, devices, home appliances, and everything else to be connected together and among themselves and exchange data over the Internet. Currently, we are in the beginning stages of IoT. The number of devices capable of this was 8.4 billion in 2017. It is actually expected for it to rise to a staggering 30 billion by the end of 2021. That is a lot of smart devices.

Expert View on The Future of Data Science

Data science solutions have been widely adopted by leaders and starters covering various business fields. It is a combination of math, art, and logic used for bettering and accelerating the development of industries. Moreover, the data scientist career path has been one of the highest-paid, most attractive, and respected. In this article, we will describe what the future holds for data science based on the Light IT data engineering company’s opinion. The experts of this company shared their views on six key transformations to expect, so let’s get started.

1. The amount of data will only be increasing

Technological progress will only encourage people to consume more information and spread more information about themselves. In 2020, a user generates about 2 megabytes of data per second. For this reason, specialists find new ways to store large amounts of information. The currently used methods of storing information look like this now:

Hybrid storage. Cloud isn’t reliable enough to store confidential information about companies, so the most private information would be kept on portable drives, safes, lockers, etc. New ways to improve password practices and restricted access have yet to come.

Source:consoltech.com

Multi-cloud storage. Some companies must consider keeping data in multiple cloud environments, both public and private.

2. Data science will become automated

Specialists find new methods of automating data science. Even though it is meant to automate and optimize various business areas, integration and maintenance of its developments are not automated just yet. Almost everything in this field is on its way to automation, from storing and refining data to final modeling.

Experts focus on making neural networks self-learn and improve feature engineering to save time on further developments. As an example, Google is actively investing in Cloud AutoML that automates the training and designing of models. Some companies make data management more convenient for data scientists to let them work faster and easier.

3. Machine learning will stay the most wanted for business

Source:pcmag.com

Machine learning will be one of the trendiest implementations in the years to come. It covers a great variety of progress-driven tasks such as video, audio, image recognition, neural networks, text analysis, and natural language processing. Specialists put a lot of effort into reinforcement learning, which allows algorithms to work along the sequence of actions successfully. We can see it from how AI plays computer and board games.

Business process automation and forecasts using machine learning are other trends, from which industry players get strategically-important benefits. These benefits let companies reduce the costs of warehousing, logistics, staff, and eliminate human-generated mistakes. Predictive modeling will improve to provide more accurate insights on customers and competitors. Specialists also focus on conversational analytics to make chatbots and voice assistants more efficient to make customer service better.

4. Demand for skilled data scientists will only be growing

Data science is a popular career path throughout the world because specialists can work on exciting projects, implement the most incredible ideas, solve problems, and get paid well. Still, even the most developed countries worldwide have a significant shortage of employees specialized in data science. In 2019, the US companies were seeking over 2 million specialists in this field of knowledge.

Although the IT and digital sectors have most data engineers hired, they also work in other industries such as retail, healthcare, entertainment, transportation, manufacturing, and many more. Apart from data science or machine learning departments, scientists  in this field are engaged in the product, marketing, content, and game development teams.

5. Data security and privacy are expected to improve

Source:csoonline.com

Since massive amounts of data are generated throughout the world every year, some of it can be vulnerable to various types of security and privacy threats. Currently, there are three more specific reasons why data security has yet to be improved:

Lack of well-educated cybersecurity talents;

More elaborate and devastating cyberattacks;

Ignoring or poor awareness of regulatory standards.

However, the number of security-aware users is small. People don’t want to invent complicated and reliable passwords and read privacy policies. As data science evolves, we can expect security and privacy protocols to change according to the applicable laws at locations where they operate. Still, users must also show higher carefulness and awareness of the law to avoid data theft.

6. Fast and actionable data will outshine big data

Event-driven applications help digital enterprises instantly identify and respond to new opportunities or threats. Many businesses focus on fast data, which is far ahead of stream-based analytics. It allows specialists to analyze both current and historical data simultaneously, thus helping to navigate and respond to a particular situation effectively.

Fast data can improve the quality of business analytics. Combined with an integrated development environment, built-in intelligent data processing, and machine learning, fast data simplifies various processes and also starts a new era of business intelligence.

Source:linkedin.com

Conclusion

The critical areas of data science transformations include its automation, the creation of reliable storage for growing amounts of information, and keeping up machine learning best practices. Experts also realize the need to create more skillful talents, enhance privacy and security, and shift focus from big data to fast data. The sector’s changes are about to come gradually, and the hardest part of it would be dealing with security matters in different countries. As data science improves, cyber threats become harder to prevent, so companies must think about making faster progress with data.