Tag Archives: machine-learning

3 February 2024
4 Comments
AI, Business

Machine Learning in Business Analytics

Reading Time: 2 minutes
What is business analytics? Using data to improve business outcomes | CIO

Analytics is an essential part of every business. It helps to assess a market and company’s sales, identify customers’ needs and modern trends, realize which products or services of an organization are in demand, and overall gives a perspective on possibilities of growth. Machine learning for analytics is the process of using ML algorithms to aid the analytics process of evaluating data and discovering insights with the purpose of making decisions that improve business outcomes.

Customer Segmentation

Machine learning algorithms can automatically segment customers into distinct groups based on various criteria, such as purchasing behavior, location, or product preferences. This segmentation allows marketers to target each group with highly relevant content and offers.

Predictive Analytics

Machine learning models can predict future customer behavior, such as which products of the company a customer is likely to purchase next or when they are most likely to make a purchase. This information enables businesses to time their marketing campaigns effectively.

Demand Anticipation

By analyzing historical sales data, competitor activity, and external factors like weather and economic trends, ML models can predict future demand with remarkable accuracy. This empowers businesses to optimize inventory levels and respond effectively to fluctuating market conditions.

Personalized Recommendations

You’ve probably seen personalized product recommendations on e-commerce websites like Amazon. Machine learning algorithms analyze a customer’s past behavior and recommend products or content that are most likely to interest them, increasing the chances of conversion.

Fraud Detection

Machine learning-based fraud detection systems rely on ML algorithms that can be trained with historical data on past fraudulent or legitimate activities to autonomously identify the characteristic patterns of these events and recognize them once they recur.

Moreover, by analyzing transaction patterns and identifying anomalies of a particular entity, ML models can flag suspicious activity in real-time, preventing fraudulent transactions and mitigating financial losses. This proactive approach safeguards not only businesses but also their customers, fostering trust and security.

Operations Optimization

ML algorithms can analyze vast operational data to identify bottlenecks, inefficiencies, and potential areas for improvement. This allows businesses to optimize resource allocation, scheduling, and logistics, leading to cost savings and increased productivity.

Employee Performance and Human Resources

Machine learning can be used in HR analytics to assess employee performance, predict employee turnover, and identify factors contributing to job satisfaction. This helps in making data-driven decisions related to workforce management and employee engagement.

Text Analytics

Machine learning models can analyze text data from sources like social media, customer reviews, and surveys to gauge sentiment. This information is valuable for understanding public opinion, improving customer satisfaction, and managing brand reputation.

These are some functions of machine learning in business analytics. It’s a very powerful tool which sheds light on the market and ongoing processes in economy, resulting in enhanced accuracy of predictions and, therefore, contributes to the success and margins of a company.

Sources:

  1. https://www.techtarget.com/searchenterpriseai/feature/10-common-uses-for-machine-learning-applications-in-business
  2. https://www.linkedin.com/pulse/role-machine-learning-personalized-marketing#:~:text=Machine%20Learning’s%20Contribution&text=Machine%20learning%20algorithms%20can%20automatically,highly%20relevant%20content%20and%20offers.
  3. https://www.itransition.com/machine-learning/fraud-detection#:~:text=Machine%20learning%2Dbased%20fraud%20detection,recognize%20them%20once%20they%20recur.
  4. https://www.oracle.com/business-analytics/what-is-machine-learning-for-analytics/#:~:text=Machine%20learning%20for%20analytics%20is,Providing%20analytics%2Ddriven%20insights.
  5. https://bard.google.com/chat/616ccd3957c0cc71 (as a source for some features of ML)

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29 January 2023
1 Comment
AI, Business

Unlocking the Power of Machine Learning in Ecommerce

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https://www.prefixbox.com/blog/machine-learning-for-ecommerce/

Machine learning has become an integral part of the ecommerce industry. It is a powerful tool that helps businesses make more optimal decisions and improve their overall performance. It already has a plethora of implementations, from demand forecasting and inventory management, through dynamic pricing and personalized ad targeting, to chat bots or picture generators. Nevertheless, in this blog post, I will explain two of the most common implementations of machine learning in ecommerce, specifically forecasting demand and pricing strategies.

Forecasting demand is a crucial aspect of ecommerce, as it allows to predict future sales and adjust the inventory accordingly. Machine learning algorithms can analyze large amounts of historical data to identify patterns and trends that would be difficult for humans to spot, which can then be used to make accurate predictions about future demand. For example, retailers can use time series forecasting models or regression models, which take into account historical data collected, market trends, and consumer behavior to predict how many units of a particular product will be sold in the future, which they can also use to help with inventory management. Although the accuracy depends on the amount and quality of data collected, this can help avoid stockouts, which can lead to lost sales, or overstocking, which can result in wasted resources. Additionally, retailers can use machine learning to forecast demand for new products, which can help them make more informed decisions about when to introduce new items and how much inventory to stock.

Pricing is a critical component of any retail business, as it directly impacts customer behavior and ultimately affects a retailer’s bottom line. Traditionally, retailers have relied on manual methods, their experience, and gut instincts to set prices, but these methods can be time-consuming and prone to error. Since experience is a component there, you can improve it by having more data, and thus this is another area where machine learning can make a significant impact. By analyzing data on competitor prices, consumer behavior or demand spikes, retailers can use machine learning to optimize their pricing strategies and maximize their profits. For example, retailers can use dynamic pricing algorithms, or data-driven pricing strategies, which adjust prices in real-time based on factors such as supply and demand, to ensure that prices are always competitive and to maximize revenue, a strategy that Amazon is using since 2013. Additionally, retailers can use machine learning to analyze customer reviews, ratings and feedbacks to determine which products are most popular and adjust their pricing accordingly.

Price optimization with machine learning

To sum up, machine learning is a powerful tool that helps ecommerce businesses improve their overall performance. By analyzing vast amounts of data available to them, with the ever-increasing data collection practice, machine learning is becoming an essential tool to gain an edge and stay competitive in the ecommerce industry, and as the technology continues to evolve, it is only likely that in the coming years, we will see even more innovative applications of machine learning.

Sources:

https://mobidev.biz/blog/machine-learning-methods-demand-forecasting-retail

https://tryolabs.com/blog/price-optimization-machine-learning

https://www.sciencedirect.com/science/article/pii/S187705091401309X

https://www.prefixbox.com/blog/machine-learning-for-ecommerce/

https://www.hbs.edu/faculty/Pages/item.aspx?num=49523

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17 January 2023
2 Comments
AI, Business

Revolutionizing the Workplace: The Impact of artificial intelligence and machine learning

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Artificial Intelligence – Changing the Landscape for Businesses
https://www.cubix.co/blog/artificial-intelligence-changing-the-landscape-for-businesses

Artificial intelligence (AI) and machine learning (ML) from self-driving cars, through picture and text generators, to virtual personal assistants, are making significant strides in a wide range of industries quickly revolutionizing the way businesses operate.

AI and ML are being used to automate repetitive tasks, freeing up employees to focus on more complex and strategic work. This can lead to increased productivity and cuts in costs. In addition, AI and ML can be used to analyze large amounts of data, providing insights that were previously not possible. This can help organizations make better decisions and improve their bottom line.

One example of how AI and ML are being used in the workplace is in the field of human resources. AI-powered chatbots can be used to screen job candidates, conduct initial interviews, and even schedule follow-up interviews. This can save HR teams a significant amount of time and resources. Additionally, AI and ML can be used to analyze employee data, such as performance reviews, to identify patterns and trends that can help managers make better decisions about promotions, bonuses, and other HR-related issues.

Another area where AI and ML are making a big impact is in customer service. Virtual personal assistants and chatbots can be used to answer customer questions, resolve issues, and even make recommendations. The times when just hearing an automatic voicemail script, had frustrated to no end every person trying to call a helpdesk are coming to an end. Additionally, AI and ML can be used to analyze customer data and predict future needs, allowing companies to proactively address issues and improve their overall customer experience, but it is already being implemented into manufacturing, finance, healthcare, and many other industries. 

However, it is also very important to consider the ethical and societal implications of AI and ML in workplaces. These technologies can lead to job displacements, bias, and lack of transparency. Therefore, it is essential for companies to consider these issues and actively work on mitigating them.

It’s clear that the impact of AI and ML on workplaces will continue to grow in the coming years. Businesses that embrace these technologies will be well-positioned to stay competitive and succeed in the future. And it will be especially true for early adapters, who will gain significant advantage over anyone that will be late for this express train. 

In conclusion, AI and ML are revolutionizing the way we work and live. Businesses that embrace these technologies will be well-positioned to stay competitive in the future. However, it is important to consider the ethical and societal implications of AI and ML in the workplace and actively work to mitigate them. And just like in 18th century when new technologies created Industrial Revolution, like 20th century computers and automation, and just like the emergence of the internet, all pushing businesses and workplaces to adapt and evolve, while rising quite similar ethical concerns, artificial intelligence and machine learning are just impossible to ignore.

Sources:

https://www.ibm.com/watson/about

https://www.mckinsey.com/capabilities/quantumblack/our-insights/ask-the-ai-experts-what-are-the-applications-of-ai

https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-modeling-the-impact-of-ai-on-the-world-economy

https://www.beekeeper.io/blog/3-reasons-you-want-ai-in-the-workplace/

https://www.bbvaopenmind.com/en/articles/artificial-intelligence-in-workplace-what-is-at-stake-for-workers/

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17 October 2022
3 Comments
AI

How could AI improve healthcare?

Reading Time: 3 minutes

Artificial Intelligence in today’s world is developing rapidly. It is expected that by 2025 AI systems market will reach 791.5 billion dollars in revenue. In my article I want to focus on how AI is affecting the world’s health care. And I’m not going to show you a super fancy robot that will replace every doctor and even perform surgeries, but something that is way more accessible to people around the globe. 

Ada Health is a free medical, symptom-checking app. It helps you check your symptoms and discover what might be causing them. With the help of Artificial Intelligence, Ada compares your case with thousands of medical documents and conditions to give you the most possible causes. The app is available in seven different languages: English, German, Portuguese, Spanish, French, Swahili, and Romanian. Swahili and Romanian were added thanks to funding from Fondation Botnar. It gives 119 million people more access to medical guidance. Ada has now over 12 million users and completed 28 million symptom checks.

How does a symptom check work?

The app is designed like a chatbot. First you have to start symptom assessment. The AI gets smarter the more you tell so you are asked a few simple questions (name, gender, date of birth and some personal questions about your health state). Then you can choose whether this assessment is for yourself or someone else.  From there you are searching for your symptoms and briefly describe it. If you don’t know any of the medical terms you have the ability to check it within the app (short explanation with a picture). When all the questions are finished Ada’s AI processes your answers and you get your report. Note that this is not a medical diagnosis, but only a suggestion what might be the cause and suggests what you could do. 

Ada is providing people with more information about their current state and suggest taking better health related actions. With the use of Swahili, it will be a game changer in developing countries of Africa, where people don’t have access to proper healthcare, or it is too expensive for them. The app will make them aware of their own health. 

Ada is doing a great job at what it is supposed to do, but there is still a problem with accessibility of healthcare. Governments in developing countries should work with initiatives like this one and develop a new healthcare system. In my opinion apps like Ada should be used to interview and diagnose. Then a person would only go for a quick examination and receive medical advice from a doctor. It could make the poor systems more efficient, and one doctor would serve more people at the time.

In the future the app should be developed to gain and use even more personal information about one’s health. It could involve congenital diseases, allergies, eating habits, sports activity, but also information gathered everyday thru your smart devices. It could be integrated into your personal medical system that guides you with every aspect of your health.

Thank you for your time. Let me know what you think about this project and could it actually improve healthcare?

Sources:

https://ada.com/about/

https://www.cnbc.com/2021/05/27/samsung-and-bayer-invest-in-ai-doctor-app-ada-health.html

https://www.idc.com/getdoc.jsp?containerId=US49571222

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16 January 2022
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Grammarly – a helping hand at improving your English grammar

Reading Time: 3 minutes

by Lev Hladush

Grammarly is both the name of San-Francisco based company and their main product – a communication assistant that helps correct grammar and typos in word processing to any internet user.

What is especially exciting about Grammarly is that the work of their assistant relies heavily on Artificial Intelligence. Thus making it a particular object of interest for us, students of Management and Artificial Intelligence program. Grammarly uses AI to help millions of people worldwide make their communication clear, effective and error-free. Everyone knows that communication is key to both personal and professional success and the mission of the company to improve lives by improving communication. The big vision behind it is to help people articulate their thoughts in a way that’s clear and effective, in a way that makes them understood as intended.

Core to this mission has been the work in natural language processing (NLP). They rely on their team’s deep expertise in NLP, machine learning (ML) and AI. The way it works is something like this:

Broadly speaking, an artificial intelligence system mimics the way a human would perform a task. AI systems achieve this through different techniques. Machine learning, for example, is a particular methodology of AI that involves teaching an algorithm to perform tasks by showing it lots of examples rather than by providing a series of rigidly predefined steps.

Grammarly’s AI system combines machine learning with a variety of natural language processing approaches. Human language has many levels at which it can be analyzed and processed: from characters and individual words through grammatical structures and sentences, even paragraphs or full texts. Natural language processing is a branch of AI that involves teaching machines to understand and process human language (English, for instance) and perform useful tasks, such as machine translation, sentiment analysis, essay scoring, and, in our case, writing enhancement.

An important part of building an AI system is training it. AIs are kind of like children in that way. Kids learn how to behave by watching the people around them and by positive or negative reinforcement. As with kids, if you want your AI system to grow up to be helpful and functional, you need to be careful about what you expose it to and how you intervene when it gets things wrong.

The first step is choosing high-quality training data for your system to learn from. In Grammarly’s case, that data may take the form of a text corpus—a huge collection of sentences that human researchers have organized and labeled in a way that AI algorithms can understand. If you want your AI to learn the patterns of proper comma usage, for example, you need to show it sentences with incorrect commas, so it can learn what a comma mistake looks like. And you need to show it sentences with good comma usage, so it learns how to fix comma mistakes when it finds them.

AI systems also need feedback from humans. When lots of users hit “ignore” on a particular suggestion, for example, Grammarly’s computational linguists and researchers make adjustments to the algorithms behind that suggestion to make it more accurate and helpful.

Just like people, AI does sometimes make errors. It’s especially possible when an AI is facing a situation it doesn’t have much experience with. Grammarly is trained on naturally written text, so it’s good at spotting issues that occur naturally when people write. It’s less good at handling sentences where mistakes have been deliberately inserted because they often don’t resemble naturally occurring mistakes.

Sources: Grammarly.com

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16 January 2020
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DeepL – a translator which surpassed Google Translate

Reading Time: 4 minutesA company doesn’t have to be a technological giant to create a product that exceeds the most popular programs of the same type. There is no doubt that in the world of automatic translation Google, Microsoft, and Facebook are the leaders. And yet it turns out that a small company DeepL has created a translator that sometimes exceeds the quality of the most popular programs of this type.

DeepL logo
Source: https://www.deepl.com/home

 

How DeepL was created?

It turns out that the key to the development of the translation service was to use the first own product, which is Linguee, a translation search engine on the Internet. The data obtained in this way became training material for artificial intelligence behind DeepL.

Interestingly, Linguee’s co-founder, Gereon Frahling, once worked for Google Research but left in 2007 to continue his new venture.

Currently, DeepL supports 42 language combinations between Polish, English, German, French, Spanish, Italian and Dutch. Already now, artificial intelligence is learning more, such as Mandarin, Japanese and Russian. There are plans to introduce an API, by means of which it will be possible to develop new products and implement the mechanism in other services.

The team has been working with machine learning for years, for tasks bordering on basic translation, but finally, they began a fervent work on a completely new system and a company called DeepL.

 

What is the advantage of DeepL?

Once again, people realized that AI is learning all the time – to the benefit of consumers, of course. The artificial intelligence behind the DeepL not only accurately recognizes words and selects translations, but is also able to understand certain linguistic nuances, perfectly copes with changed sentence patterns, which makes the result of a user’s inquiry extremely natural – as if it was written by a human being.

The company also has its own supercomputer, which is located in Iceland and operates at 5.1 petaflops. According to press releases with such equipment DeepL is ranked 23rd in the Top 500 supercomputers worldwide.

 

The statistics do not lie

The blind test compared the new product and solutions from Google, Facebook, and Microsoft. Professional translators were supposed to choose the best results of the mechanisms in the comparison without knowing the author of the translations:

DeepL’s blind testing results
Source: https://techcrunch.com/2017/08/29/deepl-schools-other-online-translators-with-clever-machine-learning/

 

But that’s not all, because in the BLEU results DeepL also gets great scores. BLEU is an algorithm for evaluating the quality of translation.

 

Why do others recommend DeepL instead of Google Translate?

The main advantage of DeepL in the context of Google Translate is much better knowledge (or rather a detection) of idioms, phrases, and phraseological compounds. Where, for example, Google Translate is weakening and literal meaning is being found, DeepL can surprisingly offer a more nuanced and much more specific language solution. The translation is not a literal translation of the text, but one that best harmonizes with the contexts and connotations characteristic of the words.

The passage from a German news article rendered by DeepL
Source: https://techcrunch.com/2017/08/29/deepl-schools-other-online-translators-with-clever-machine-learning/

The passage from a German news article rendered by Google Translate
Source: https://techcrunch.com/2017/08/29/deepl-schools-other-online-translators-with-clever-machine-learning/

 

No wonder that DeepL is gaining recognition all over the world. Here are some reviews:

Thanks to more French-sounding phrases DeepL has also surpassed other services. – Le Monde, France

In the first test, from English to Italian, it was very accurate. In particular, he understood the meaning of the sentence well, instead of being stunned by the literal translation. – La Repubblica, Italy

DeepL from Germany surpasses Google Translate. A short WIRED test shows that the results of DeepL are by no means worse than those of its best competitors, and in many cases even surpass them. Translated texts are often much more fluid; where Google Translate creates completely meaningless word strings, DeepL can at least guess the connection. – WIRED.de, Germany

We were impressed with how artificial intelligence selects the translations and how the results of its work look afterward. Personally, I had the impression that on the other side sits a man who on speed translates. – Antyweb, Poland

 

The DeepL tool has been made available to a wider audience – for free in the form of a website.

Now it is only a matter of waiting for DeepL to advertise its tool, because although it does not have a large language base, at first glance the accuracy of the translations definitely exceeds the most popular tools of this type.

It’s worth watching how the product will develop further as the current achievements of DeepL are really promising.

Did any of you choose DeepL instead of Google Translate?

 

References:

[1] https://techcrunch.com/2017/08/29/deepl-schools-other-online-translators-with-clever-machine-learning/

[2] https://www.deepl.com/blog/20180305.html

[3] https://www.dw.com/en/deepl-cologne-based-startup-outperforms-google-translate/a-46581948

[4] https://www.forbes.com/sites/samanthabaker1/2019/06/27/will-this-german-startup-win-the-translation-game/

[5] https://www.deutsche-startups.de/2018/07/05/deepl-koelner-uebersetzungskoenig-macht-millionengewinn/

[6] https://www.forbesdach.com/artikel/davids-erbe-und-igels-strategie.html

[7] https://www.letemps.ch/societe/deepl-meilleur-traducteur-automatique

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6 January 2020
2 Comments
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Duolingo – the best machine learning startup to work for in 2020

Reading Time: 3 minutesDuolingo is starting the year off strong. They have been named one of the top startups to work for, in the growing field of machine learning. These and many other insights are from a Crunchbase Pro analysis completed using Glassdoor data to rank the best machine learning startups to work for in 2020. Why is Duolingo a unique company?

Duolingo logo
Source: https://www.duolingo.com/

Duolingo AI Research

Duolingo AI Research is one of Duolingo’s fastest-growing teams. They are using real-world data to develop new hypotheses about language and learning, test them empirically, and ship products based on their research. Duolingo has revolutionized language learning for more than 300 million people around the world. They keep on bringing creative, interdisciplinary ideas on how to deliver a high-quality education to anyone, anywhere, through AI.

 

Duolingo AI team logo
Source: https://research.duolingo.com/

 

Tools and data from Duolingo

Duolingo use AI to adapt longer learning content to learners’ level. The startup is regularly releasing their internal tools to the public so everyone can read more about their research innovations. One of them is CEFR Checker. This tool determines whether texts are appropriate for beginner, intermediate, or advanced learners of English or Spanish. It works by analyzing vocabulary and highlighting words by their reading proficiency level according to the Common European Framework of Reference (CEFR). Duolingo uses interactive tools like this one to help people revise content (e.g., Podcasts and Stories) for particular levels.

The Duolingo CEFR Checker: an AI tool for adapting learning content
Source: https://making.duolingo.com/the-duolingo-cefr-checker-an-ai-tool-for-adapting-learning-content

The Duolingo CEFR Checker: an AI tool for adapting learning content
Source: https://making.duolingo.com/the-duolingo-cefr-checker-an-ai-tool-for-adapting-learning-content

 

Duolingo is also committed to sharing data and findings with the broader research community. SLAM Shared Task is an example project. It contains data for the 2018 Shared Task on Second Language Acquisition Modeling (SLAM). This corpus contains 7 million words produced by learners of English, Spanish, and French. It includes user demographics, morph-syntactic metadata, response times, and longitudinal errors for 6k+ users over 30 days.

 

Why people should consider working at Duolingo?

The language-learning app Duolingo is valued at $1.5 billion after a $30 million investment by Alphabet’s CapitalG. Bookings growth has risen from $1 million to $100 million in less than three years for the most downloaded and top-grossing education app worldwide. What is more, Pittsburgh’s first venture capital-funded $1 billion start-up plans to increase staff by 50% with the new funding. Duolingo has been adding user and revenue at an impressive pace, continuing to solidify its position as the No. 1 way to learn a language globally.

 

Why people should consider working in the machine learning field?

Demand reminds high for technical professionals with machine learning expertise. According to Indeed, Machine Learning Engineer job openings grew 344% between 2015 to 2018 and have an average base salary of $146,085 according to their Best Jobs In The U.S. Study.

It can be safely stated that Duolingo is developing very dynamically. There is also no doubt that the rapid growth of a startup also means the development of its employees.

Would you choose to join Pittsburgh’s unicorn if you had such a chance? What do you think about Duolingo’s contribution to the development of the education sector?

 

References:

[1] https://www.forbes.com/sites/louiscolumbus/2020/12/29/the-best-machine-learning-startups-to-work-for-in-2020-based-on-glassdoor/#71505e744886

[2] http://blog.indeed.com/2019/03/14/best-jobs-2019/

[3] https://www.cnbc.com/2019/12/03/google-funded-duolingo-first-1-billion-start-up-from-pittsburgh.html

[4] https://making.duolingo.com/the-duolingo-cefr-checker-an-ai-tool-for-adapting-learning-content

[5] https://making.duolingo.com/how-machine-learning-helps-duolingo-prioritize-course-improvements

[6] https://cefr.duolingo.com/

[7] https://research.duolingo.com/

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1 January 2020
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Spotify’s Tastebuds tool will enhance your social music discovery

Reading Time: 3 minutesThe new function aimed at Spotify will allow us to slightly broaden our musical tastes. All thanks to our friends from the platform.

New Spotify’s feature logo
Source: https://techcrunch.com/2019/12/18/spotify-tastebuds/

 

How music sharing options look like now?

Thinking about what Spotify can offer today, we can mention social media integration. It is a popular feature that enables users to connect their Spotify accounts to their Facebook and Twitter profiles. That lets them access their friends’ favorite music and playlists and share their choices with others as well. Individuals can create, share, and edit playlists with other listeners. If users want recommendations, they can integrate their system with Last.fm, an application that provides music recommendations based on listening history. However, this is an external application that does not belong to Spotify. You can also view a Friend Activity ticker of songs your Facebook friends are currently listening to on the desktop app. You can search for specific users and follow them or view playlists they’ve made public too. Spotify doesn’t promote user search much anyway.

 

Why Spotify need a change?

Sharing playlists on Spotify is not a problem, but when it comes to speeding up the entire algorithm, things get complicated quickly. If we listen to music in a random way, it is known that Spotify will not start sending us recommendations based on it. If that were the case, playlists created using algorithms would not suit certain user’s tastes. Social sharing has never been the main priority for Spotify. The Activity Feed, which shows what your friends are currently listening to, is limited to the desktop version of Spotify. The in-app messenger for sharing music was nixed in favor of letting users share songs on social media or on their Instagram Stories. Apparently, that was a mistake as far as we know that Tastebuds is coming.

Tastebuds feature on Spotify
Source: https://techcrunch.com/2019/12/18/spotify-tastebuds/

 

But what will Tastebuds really give us?

As the information on the site is telling: Tastebuds will let your friends discover music that you trust. This description appears in the tab that has not yet been launched, but the developers have already sewn it in the application – in the left column, next to the Home Page, Browse and Radio. The prototype feature was discovered in the web version of Spotify by reverse engineering sorceress Jane Manchun Wong. She gave some more details on how it works. By tapping on the pen icon, users can view information about what their friends have been playing most. Then, they can easily listen along or add songs to their own library.

Tastebuds feature discovered in the web version of Spotify thanks to reverse engineering
Source: https://techcrunch.com/2019/12/18/spotify-tastebuds/

Spotify Tastebuds code
Source: https://techcrunch.com/2019/12/18/spotify-tastebuds/

 

It remains to wait for deployment

When will the new feature come into effect? There is no official information about it. A Spotify spokesperson confirms that they are always testing new products and experiences, but have no further news to share with the audience. For now, anyone can access a non-functioning landing page for the feature at https://open.spotify.com/tastebuds. Tastebuds could be a rebranded version of the Friends Weekly playlist that was discovered in May 2019. Whatever it may be, the test could be a sign of more social listening features to come.

Social is a huge but under-tapped opportunity for Spotify. Social recommendations could get users listening to Spotify for longer. While competitors like Apple Music or YouTube might offer similar music catalogs, users won’t stray from Spotify if they become addicted to social discovery through Tastebuds.

Do you think Tastebuds is just what Spotify needs? Maybe something else would make the application more user-friendly? Share your opinion.

 

References:

[1] https://www.crunchbase.com/organization/spotify#section-overview

[2] https://rms.pl/aktualnosci/sprzet/3239-spotify-tastebuds

[3] https://www.spidersweb.pl/2019/12/spotify-tasetbuds-czego-sluchaja-znajomi.html

[4] https://www.theverge.com/2019/12/18/21028474/spotify-tastebuds-playlist-friends-music-discovery-social-sharing-feature

[5] https://techcrunch.com/2019/12/18/spotify-tastebuds/

[6] https://www.engadget.com/2019/12/19/spotify-social-music-discovery-tastebuds/

[7] https://www.theverge.com/2018/5/9/17337182/spotify-testing-new-friends-weekly-playlist

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19 January 2019
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The use of facial recognition technology on birds

Reading Time: 2 minutes

Today, I want to demonstrate you a great example of how object recognition technologies based on machine learning:

1) becoming widely available and do not require rare genius programming skills to get the result.

2) can be greatly trained even on a very modest in size data sets.

The article, that I have read some time ago, tells how a bird lover and part-time computer science professor, together with his students, taught the neural network to recognize the bird species and then — and that impressed me a lot – to distinguish individual species of woodpeckers, who flew to the bird feeder in his yard.

At the same time, 2450 photos in the training sample were enough to recognize eight different woodpeckers. The professor estimated the cost of a homemade station for the recognition and identification of birds at about $ 500. This is really can be called technology for everyone and machine intelligence in every yard.

Moreover, this technology can really help birds. As Lewis Barnett, the inventor of this technology wrote in his article : «Ornithologists need accurate data on how bird populations change over time. Since many species are very specific in their habitat needs when it comes to breeding, wintering and migration, fine-grained data could be useful for thinking about the effects of a changing landscape. Data on individual species like downy woodpeckers could then be matched with other information, such as land use maps, weather patterns, human population growth and so forth, to better understand the abundance of a local species over time»

As some people correctly noted, this technology has also some great commercial potential. Just imagine that camera traps will be able to recognize birds that harm your fruit trees and than activate  a device that make a large noise to scare away pests.

Sources:

https://theconversation.com/i-used-facial-recognition-technology-on-birds-106589

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2 December 2018
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How Artificial Intelligence is starting to have a serious effect on our lives?

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Have you seen Minority Report directed by Steven Spielberg? 

 

 

For those who haven’t I recommend watching it because the prophecy of this film begins to meet.

Due to the fact that technological process is constantly developing and thanks to that the meaning of Artificial Intelligence in our lives increase, we can definitely be scared about this what is happening around us.

 

Have you ever been thinking about which is one of the most intimate things in people lives?

It is sexuality orientation. Nowadays many people hide them real sexuality in fear of social indignation for example: sport players, family members, schoolmates. This people have to bother with this inside battle of “coming-out” every single day and now it is going to be worse. Nowadays the AI can guess whether you are gay or straight based of photos of your face. It is the fact not the opinion! Now we can say that machines started to be better “gaydar” than people. The study work from Stanford University – has found algorithm which could distinguish with 81% of accuracy whether you are gay or straight for men and with 74% of accuracy for women.

The algorithm was tested on machine intelligence which had to research of 35 000 facial photos from the one of dating sites and thanks to that had find out the real sexual orientation.

“The research found that gay men and women tended to have “gender-atypical” features, expressions and “grooming styles”, essentially meaning gay men appeared more feminine and vice versa. The data also identified certain trends, including that gay men had narrower jaws, longer noses and larger foreheads than straight men, and that gay women had larger jaws and smaller foreheads compared to straight women.”   – Sam Levin, The Guardian

 

Okay what if I am straight?

The authors of study which was published in the Journal of Personality and Social Psychology, Dr. Michal Kosinski and Yilun Wang, claim that this algorithm can also be used as a similar AI system which could be trained to spot others human traits such as IQ or political views. They are also warning us against this AI develop process because it can turn into something that we don’t really want to in our lives.

It is happening now!

Police in the UK are piloting a new project which provides to use AI to determines how someone is likely to commit the crime. Seems familiar? Back to that what I wrote at the beginning of my post, Steven Spielberg (Director) and Philip K. Dick (writer) were right. AI is going to prevent us from committing the crime.

 

“(…) The system has 1,400 indicators from this data that can help flag someone who may commit a crime, such as how many times someone has committed a crime with assistance as well as how many people in their network have committed crimes. People in the database who are flagged by the system’s algorithm as being prone to violent acts will get a “risk score,” New Scientist reported, which signals their chances of committing a serious crime in the future. (…)

(…) Donnelly told the New Scientist that they don’t plan to arrest anyone before they’ve committed a crime, but that they want to provide to those who the system indicates might need it. He also noted that there have been cuts to police funding recently, so something like NDAS (National Data Analytics Solution) could help streamline and prioritize the process of determining who in their databases most needs intervention. (…)”
– Melanie Ehrenkranz, gizmodo.com

The project now is in its infancy in comparison to how important it can be for the future of the justice system.

To sum up my post, there are billions of facial images of people that are publicly available on social media sites, government databases and also these ones which come from the streets cameras. In my opinion we should try to care more about our privacy in a media and don’t let the governments to have that serious impact on our lives because as we know the systems are like people, they sometimes fail.

 

 

 

Sources:

https://bit.ly/2Pg2urN
https://bit.ly/2BMzAMk
https://bit.ly/2AHTgiE
https://bit.ly/2AGC9Og
https://bit.ly/2So735a

Photos:

https://bit.ly/2KONqAT
https://bit.ly/2BL0jJ1
https://bit.ly/2AHX9Eg
https://bit.ly/2riurpi

author: Michał Żelazo

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