Kozminski Techblog

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The rapid progress in artificial intelligence (AI) has the potential to revolutionize the way we grow and manage our food. With its ability to process vast amounts of data and make predictions, AI is increasingly being seen as a tool for making agriculture more efficient, sustainable and profitable. In this article, we will explore the ways in which AI is being applied in agriculture and the challenges that must be overcome in order to realize its full potential.

Precision Agriculture One of the most promising applications of AI in agriculture is precision agriculture. By analyzing large amounts of data from sources such as weather sensors, soil sensors, and satellite imagery, AI algorithms can help farmers make informed decisions about when and how to plant, fertilize, and irrigate their crops. This results in better crop yields and a reduction in waste, which can translate into increased profits.

Livestock Management AI is also being used to monitor the health and behavior of livestock. By analyzing data from wearable devices or cameras, farmers can monitor the well-being of their animals and detect potential health problems early on. This can lead to improved animal welfare and increased productivity.

Predictive Maintenance Another important application of AI in agriculture is predictive maintenance. By analyzing data from equipment sensors, AI algorithms can predict when maintenance is needed, reducing downtime and costs. This not only improves the efficiency of the equipment but also helps to avoid expensive repairs and replacements.

Crop Monitoring AI is also being used to monitor crop health and detect diseases and pests. By analyzing images captured by drones, AI algorithms can identify problems early on, which can help farmers take action before it is too late. This can result in reduced crop loss and increased yields.

Supply Chain Management Finally, AI is also being used to manage the agriculture supply chain. By predicting demand for crops, AI algorithms can help farmers optimize their production and distribution, reducing waste and increasing profitability.

Challenges and Ethical Considerations While the potential benefits of AI in agriculture are significant, there are also a number of challenges that must be overcome. One of the biggest challenges is the availability and quality of data. In order to realize the full potential of AI in agriculture, high-quality data must be collected and made available to farmers.

Another important consideration is privacy and ethics. There are concerns about the use of AI algorithms in agriculture, such as the potential for biased decision-making or the displacement of human workers. These concerns must be addressed in order to ensure that AI is used in an ethical and responsible manner.

In conclusion, AI has the potential to transform the agriculture industry, making it more efficient, sustainable, and profitable. However, to realize this potential, the challenges and ethical considerations must be addressed. With the right approach, AI has the potential to make a positive impact on the agriculture industry and help to ensure a sustainable future for food production.

References: Bryans, J., Bennett, R., & Mitchell, R. (2018). Data challenges in agriculture and the food system. Trends in food science & technology, 74, 99-112. Lobell, D. B., Schlenker, W., & Costa-Roberts, J. (2011). Climate trends and global crop production since 1980. Science, 333(6042), 616-620. Liu, J., Wang, H., & Zhang, J. (2020). Artificial

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Artificial Intelligence (AI) has its origins in the 1950s, when a group of researchers at Dartmouth College in the United States proposed the idea of creating “thinking” machines that could mimic human intelligence. This led to the development of the first AI programs, such as ELIZA (a computer program that could mimic human conversation). In the decades that followed, advances in computer technology and a deeper understanding of the human brain led to further developments in AI, including expert systems, neural networks, and machine learning. Today, AI is used in a wide range of applications, from drone cars to medical diagnostics to financial analysis.

Let’s break down what role AI plays in investing. It is to provide a more efficient and accurate way to analyze large amounts of data and identify patterns that can help in the decision-making process. AI can perform a variety of tasks, the main ones we’ll highlight are the following:

• Improving efficiency: AI can process and analyze large amounts of financial data quickly and accurately, which can help investors make clear and flexible decisions.
• Strategy development: AI-driven investment strategies can potentially outperform human-invented ones because they can identify patterns and make predictions that humans are incapable of making.
• Automation: AI can automate repetitive and time-consuming tasks such as data analysis and trading, freeing up time for investment professionals to focus on more complex and valuable phases of work.
• Risk management: AI can help investors identify and manage risk by monitoring portfolios and assessing risk levels.
• Regulatory Compliance: AI can help identify suspicious activities and ensure trading activity is compliant.
• Innovation: AI can help generate new investment ideas and identify new opportunities that analysts may miss.

In the future, AI is likely to continue to grow and integrate into various aspects of the investment process. AI has the potential to improve efficiency, accuracy, and decision-making in areas such as data analysis, forecasting, and automated trading. As technology and algorithms evolve and improve, AI will become more advanced and more capable of solving more complex problems.

In addition, legislators will need to adapt and ensure that the financial industry develops and uses AI in an ethical and legitimate manner.

That said, the risks associated with the use of AI cannot be overlooked:

• Lack of transparency: AI-based investment systems may not be easy to understand, making it difficult to identify and correct potential technical errors. Lack of transparency can also make it difficult for investors to assess the risk of specific investments on the same parameter.
• Bias: As strange as it may sound, AI systems are trained on historical data, which can be biased and this can reinforce existing biases in investment decisions. This can lead to disappointing results and increase the risk of material or reputational damage.
• Over-reliance: Investors may over-rely on artificial intelligence systems and miss important information or not fully understand the basic assumptions and limitations of artificial intelligence models. This can lead to poor investment decisions.
• Cybersecurity: AI systems are vulnerable to cyberattacks and data breaches that could compromise sensitive, financial information and violate investment regulations.
• Job liberation: The growing use of AI could lead to the displacement of professionals, which could have negative consequences for the economy, such as higher unemployment.
• Regulation: As AI technology advances, regulations may not keep pace with the pace of innovation, so gaps in oversight of AI-managed systems and AI-driven decisions may arise.

Thus, AI is increasingly being used to manage and optimize portfolios, find new investment opportunities, and detect fraud. However, it is important to note that AI is not a substitute for human experience and a deep professional perspective on the specifics of economic situations is superior to AI’s capabilities. Investment professionals still need to be able to correctly interpret and understand the results of AI, as well as use their own experience when making investment decisions.

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Business loyalty to artificial intelligence technologies is growing. This is driven by the increasing availability of solutions, accumulated experience of projects and real effects of implementation.
The penetration of artificial intelligence (AI) technologies in business processes is growing worldwide. According to international company McKinsey, in 2021, 56% of global respondent companies used AI in at least one area of business, a figure that has increased by 6% since 2020. AI is predominantly used to optimize service operations (27% of companies), improve products (22%) and automate contact centers (22%).

The automotive, retail and FMCG sectors have the highest AI maturity – the degree to which they are using technology capabilities for high performance, according to international Accenture. According to the company, AI maturity reached 12% last year among the world’s largest companies, with an average of 30% of revenue generated by AI. In all, nearly 75% of the world’s major companies have integrated AI into their business strategies, according to Accenture. Forty-two percent of its respondents said the return on AI initiatives exceeded expectations.

In Russia, according to the National Research University Higher School of Economics (NRU HSE), almost one in three large businesses used AI in 2021 – primarily speech technologies (voice assistants, chatbots and other applications that work to automate the process of communication with the customer).

“Another powerful area is predictive analytics that aggregates large volumes of data,” notes Igor Pivovarov, chief analyst at the MIPT Research Center for Applied Artificial Intelligence Systems.

In some areas in large and medium-sized companies, AI is already integrated into 20% of the processes and can increase their efficiency six to seven times, said Deputy Prime Minister Dmitry Chernyshenko at the business breakfast “How to unite the business for mass implementation of AI in the industries” at the Artificial Intelligence Journey (AIJ) international conference. At the meeting, industry representatives discussed successful examples of implementing AI solutions in business.

In construction, AI helps to reduce downtime by five times, to reduce time by 48% and to reduce costs by 10-12%, said at the AIJ business breakfast Anton Elistratov, General Director of the development group of companies “Samolet”. “Gazpromneft is producing oil found by AI, says Oleg Tretiak, the company’s acting director of digital transformation. According to him, the company plans to double its investments in these technologies.

Anatoly Popov, deputy chairman of the board and head of Sberbank’s Corporate and Investment Business Block, presented at AIJ a service developed for the bank’s clients called Demand Forecasting in Manufacturing and Retail. “The accuracy of demand forecasting on the basis of AI models with details by region, time and other parameters reaches almost 100% and allows to increase profitability in trade and production,” said Anatoly Popov.

Barriers and opportunities

Artificial intelligence is becoming more accessible and efficient, say the authors of the Stanford Institute for Human-Centered AI Index 2022 report: since 2018, the cost of learning to classify images has dropped by 63.6%, and training time has fallen by 94%. Thanks to the democratization of technology, they are becoming more common in various industries – fintech, medicine, logistics, retail, industry, and marketing.

The main trend this year is customization or very simple application of industrial AI, available even to small companies, confirms Sberbank. At the bank itself, the financial impact of AI in 2021 was 205 billion rubles, the goal for this year is 230-250 billion rubles, said First Deputy Chairman of Sberbank Alexander Vedyakhin. More than 85% of client ways already contain artificial intelligence technologies, “smart” algorithms cover more than 65% of bank processes.

The services sector is the leader in implementing AI, says Konstantin Vishnevsky, director of the Institute of Statistical Studies and Knowledge Economy: the most intensive use of the technology is in the financial sector (13%) and trade (14.4%), while in the real economy (manufacturing, transport, etc.) the use of AI solutions is gradually increasing, but on average does not exceed 5%.

According to Sber forecasts, the greatest effect on gross added value by 2025 will bring the implementation of AI solutions in Russian construction (+2.1%), agriculture (+1.6%), manufacturing (+1.3%) and healthcare (+1%).

Unlike the financial, telecom and retail industries, capital-intensive industries with many complex physical assets (metallurgy, construction) implement AI technologies more slowly and the barriers are higher there, explains Alexey Masyutin, Head of the HSE AI Center.

AI projects can still be afforded mainly by major players due to high complexity of solutions, lack of dedicated staff and necessary datasets, the need to adapt AI solutions for specific tasks and radical restructuring of most business processes, commented Konstantin Vishnevsky.

One of the barriers is the cost of development and lack of ready, low-cost and convenient services that could be used “out of the box,” says Igor Pivovarov.

Growth points

Igor Pivovarov notes that in order to speed up the introduction of artificial intelligence technologies, it is impossible without investments from the state or provision of ready and available services by major players: “Support will be needed for small businesses that want to introduce AI technologies in their work, for example, by grants or tax reductions.

“If a company buys a boxed product based on AI, its implementation will require an already built IT infrastructure and data culture, and if a custom development is planned – the formation of internal competence of data researchers and ML-engineers,” adds Alexey Masyutin.

It is necessary to differentiate the processes of training of AI specialists – for example, to prepare the required number of AI engineers and AI scientists in a small number of universities – flagships in the development of breakthrough fundamental and applied AI solutions, says the head of the Center for Applied Artificial Intelligence “Skoltech” Evgeny Burnaev. At the same time it is necessary to stimulate the introduction of technologies based on AI in the real sector of the economy with scientifically and expertly proven effect from the expected implementation and its further replication.

The creation of an information resource platform that would combine both demand and supply for various solutions and developments based on AI could stimulate greater dynamics of projects using artificial intelligence in Russia, believes Alexey Masyutin: “We need our own analogue of profi.ru – profi AI.

https://www.hse.ru/news/

https://бизнеснавигатор.рф/adaptation/

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Attracting the attention of a mass audience was easier in the days when your product was an exclusive offering and when competition was relatively low. But in today’s world, the product being advertised is often just another drop in a sea of available offerings, and so the art of attracting the attention of your target audience and retaining it becomes a real challenge. It really is a Herculean task to get your message across to potential consumers and encourage them to buy the advertised product. And modern technology makes this task much easier.

Using AI in advertising
AI is now being used en masse to attract new customers. This is done through tools such as clustering and pattern matching, customizing the message with AI-powered hyper-personalization, and determining the most appropriate time and medium to communicate through pattern identification. A great example is Dixons Carphone’s strategy to increase demand for its products during Black Friday.

At a time when all the copywriters who worked on the promotion campaign were using standard slogans and expressions, the AI managed to break the pattern and come up with something new and original. This is what made the Dixons Carphone ad campaign a success.

Interestingly, the first time AI was used in an ad was in 2018 by Lexus. The AI used data from 15 years of worldwide award-winning advertising and developed a 60-second film showing a car story that brings to life and explores interesting questions about humanity and creativity and the relationship of those aspects to the AI.

How is artificial intelligence changing advertising?
AI is already revolutionizing the creation and perception of advertising. Below are some of the most popular tools for using AI in marketing.

Personalization
In the case of advertising, personalization is the acquisition of information about customers in order to increase their interest in their advertising. This data can relate to demographics, purchase intent, interests, and behaviors.

For example, younger target audiences are attracted to specific visual-oriented ads, but older target audiences prefer more detailed and thoughtful ads that focus more on products and services.

All of this helps advertisers focus on preparing just one version of an ad, which can be customized with artificial intelligence algorithms and then offered to the right target audiences.

Another lucrative personalized AI solution is conversational marketing. This approach helps advertisers maintain a personal connection with their consumers.

AI and ad creation
Interestingly, AI is now also being used to create ad copy. For example, Facebook and Instagram use AI-powered tools that help users of these platforms both create ads and offer them personalized ads.

In addition, many emerging platforms and startups are also expanding their capabilities by using AI tools to create better ads.

We also have several platforms working on fully automated advertising systems that can target markets, discover audiences, generate content, run bids and place ads.

AI is also beneficial for monitoring ad spending, tracking sales and consumer behavior. Social media platforms are also using this technology to evaluate the advertising placed on their network.

AI and effective audience segmentation
Using AI allows advertisers to identify patterns in audience behavior. This is achieved by using data such as, information about its online behavior and its preferences, browser search history and so on. All of this is used to make the right decision about what content a particular segment of potential customers prefer to see.

AI-enabled ads convert better
By analyzing data on past performance and trends, AI is able to generate insights that ensure fruitful decision-making and that your money is not wasted on poor-quality ads.

McDonald’s turned to IBM Watson Advertising to bring attention to its limited-edition McCafe specialty coffee promotion. To achieve this goal, McDonald’s used compelling and impressive backgrounds and memorable images in its ads.

McDonald’s used IBM Watson Advertising data and statistics from its stores to evaluate the results of the advertising campaign. Here they are below:

~ 5,000,000 ad impressions;
168% more effective price per visit compared to category benchmarks;
0.71% CTR for mobile branded backgrounds + 25% over the benchmark;
79% of open users visited McDonald’s restaurants within 3 days.

https://sbermarketing.ru/news/artificial_intelligence/

https://aiconference.com.ua/ru/news/iskusstvenniy-intellekt-v-marketinge-kak-ai-algoritmi-sovershenstvuyut-onlayn-reklamu-93972

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Machine learning has become one of the hottest buzzwords in technology, and it’s no wonder why. This cutting-edge field of artificial intelligence is transforming the way we live and work, and its impact is far-reaching. Whether it’s revolutionizing healthcare, finance, marketing, or even the self-driving car, machine learning is making waves and changing the game.

But what exactly is machine learning, and how does it work? Simply put, machine learning is a type of AI that allows computer systems to automatically improve their performance through experience. It does this by analyzing vast amounts of data and using statistical techniques to identify patterns and make predictions. As the system is fed more data, it continues to improve and make more accurate predictions over time.

One of the biggest players in the machine learning game is Google. From spam detection to image and speech recognition, Google is leveraging the power of machine learning to revolutionize the way we interact with technology. For example, Google’s self-driving cars use machine learning algorithms to analyze data from their sensors, including cameras and lidar, to safely navigate roads and make real-time decisions.

The healthcare industry is also benefiting from machine learning. By analyzing patient data and medical records, machine learning algorithms can help healthcare providers identify potential diseases, predict patient outcomes, and personalize treatment plans. This leads to more effective and efficient healthcare, and the potential to save lives.

In finance, machine learning is also making a big impact. By analyzing vast amounts of financial data, machine learning algorithms can identify fraudulent transactions, make more informed investment decisions, and even predict stock market trends. This leads to a more secure financial system and greater returns for investors.

And it’s not just the big industries that are reaping the benefits of machine learning. In fact, machine learning can improve our daily lives by providing personalized product recommendations, improving the accuracy of online searches, and even assisting in language translation.

The market for machine learning is growing rapidly, with the global market size valued at $1.03 billion in 2019 and projected to reach $8.81 billion by 2026. It’s clear that machine learning is here to stay, and its potential for growth is immense.

Of course, with any new technology come ethical concerns. Machine learning algorithms can sometimes be biased due to the data they are trained on, and the algorithms themselves can be designed in ways that perpetuate bias. There are also concerns about privacy and the loss of control over personal data. These are important issues that need to be addressed as machine learning continues to evolve.

In conclusion, machine learning is a game-changer in technology, and its impact is far-reaching. From revolutionizing industries to improving our daily lives, the potential for growth in this field is immense. As technology continues to advance, machine learning will play an even bigger role in shaping our future. Get ready, because the possibilities are endless!

https://ai.google/

https://www.technologyreview.com/topic/artificial-intelligence/

https://www.the-ai-institute.com/