The new issue of DataScienceDigest is here!
Machine learning in healthcare, the top 10 TED talks on AI, fraud detection in Uber, DatasetGAN, Text-to-Image generation via transformers, and more…
AI, ANN and other forms of an artificial Intelligence
As we all are aware of the fact that the digital market is heavily leaning towards a reliable UX-driven process, app development has become quite complex, especially for targeting the industry for mobile platforms.
For every organization, creating a product that is beneficial for their customer needs always comes up with a plethora of challenges.
From the technical point of time, there are various challenges that every business faces, including selecting the right platform for the app, the right technology stack or framework, and creating an app that fulfills the needs and expectations of customers.
Similarly, there are more challenges that every business faces and needs to cope with while creating its dream product.
So, what to do??
Well, what if I say that the answer to all your queries and questions is Flutter app development with Artificial Intelligence (AI) integration……
Surprised? Wondering how?
Well, AI in Flutter app development is one of the best advancements in the software market. The concept of AI was first introduced during the 20th century with loads of innovations and advancements that we are still integrating into our mobile app development.
But, what are Artificial Intelligence and Flutter app development?
I’m pleased to invite you all to enroll in the Lviv Data Science Summer School, to delve into advanced methods and tools of Data Science and Machine Learning, including such domains as CV, NLP, Healthcare, Social Network Analysis, and Urban Data Science. The courses are practice-oriented and are geared towards undergraduates, Ph.D. students, and young professionals (intermediate level). The studies begin July 19–30 and will be hosted online. Make sure to apply — Spots are running fast!
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So we have already played with different neural networks. Cursed image generation using GANs, deep texts from GPT-2 — we have seen it all.
This time I wanted to create a neural entity that would act like a beauty blogger. This meant it would have to post pictures like Instagram influencers do and generate the same kind of narcissistic texts. \
Initially I planned to post the neural content on Instagram but using the Facebook Graph API which is needed to go beyond read-only was too painful for me. So I reverted to Telegram which is one of my favorite social products overall.
The name of the entity/channel (Aida Enelpi) is a bad neural-oriented pun mostly generated by the bot itself.
I have some good news for you…
Data Science Digest is back! We’ve been “offline” for a while, but no worries — You’ll receive regular digest updates with top news and resources on AI/ML/DS every Wednesday, starting today.
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Author: Sergey Lukyanchikov, Sales Engineer at InterSystems
What is Distributed Artificial Intelligence (DAI)?
Attempts to find a “bullet-proof” definition have not produced result: it seems like the term is slightly “ahead of time”. Still, we can analyze semantically the term itself – deriving that distributed artificial intelligence is the same AI (see our effort to suggest an “applied” definition) though partitioned across several computers that are not clustered together (neither data-wise, nor via applications, not by providing access to particular computers in principle). I.e., ideally, distributed artificial intelligence should be arranged in such a way that none of the computers participating in that “distribution” have direct access to data nor applications of another computer: the only alternative becomes transmission of data samples and executable scripts via “transparent” messaging. Any deviations from that ideal should lead to an advent of “partially distributed artificial intelligence” – an example being distributed data with a central application server. Or its inverse. One way or the other, we obtain as a result a set of “federated” models (i.e., either models trained each on their own data sources, or each trained by their own algorithms, or “both at once”).
Distributed AI scenarios “for the masses”
We will not be discussing edge computations, confidential data operators, scattered mobile searches, or similar fascinating yet not the most consciously and wide-applied (not at this moment) scenarios. We will be much “closer to life” if, for instance, we consider the following scenario (its detailed demo can and should be watched here): a company runs a production-level AI/ML solution, the quality of its functioning is being systematically checked by an external data scientist (i.e., an expert that is not an employee of the company). For a number of reasons, the company cannot grant the data scientist access to the solution but it can send him a sample of records from a required table following a schedule or a particular event (for example, termination of a training session for one or several models by the solution). With that we assume, that the data scientist owns some version of the AI/ML mechanisms already integrated in the production-level solution that the company is running – and it is likely that they are being developed, improved, and adapted to concrete use cases of that concrete company, by the data scientist himself. Deployment of those mechanisms into the running solution, monitoring of their functioning, and other lifecycle aspects are being handled by a data engineer (the company employee).
Technology is as adaptable and compatible as mankind; it finds its way through problems and situations. 2020 was one such package of uncertain events that forced businesses to adapt to digital transformation, even to an extent where many companies started to consider the remote work culture to be a beneficiary long-term model. Technological advancements like Hyper automation, AI Security, and Distributed cloud showed how any people-centric idea could rule the digital era. The past year clearly showed the boundless possibilities through which technology can survive or reinvent itself. With all those learnings let's deep-dive and focus on some of the top technology trends to watch out for in 2021.
There’s a lot of talk about machine learning nowadays. A big topic – but, for a lot of people, covered by this terrible layer of mystery. Like black magic – the chosen ones’ art, above the mere mortal for sure. One keeps hearing the words “numpy”, “pandas”, “scikit-learn” - and looking each up produces an equivalent of a three-tome work in documentation.
I’d like to shatter some of this mystery today. Let’s do some machine learning, find some patterns in our data – perhaps even make some predictions. With good old Python only – no 2-gigabyte library, and no arcane knowledge needed beforehand.
Interested? Come join us.
With an estimated global population of 7.7 billion, 25 percent of people shopping through e-commerce stores. According to Statista, 52% of e-commerce stores will have omnichannel capabilities by 2020 which means they can communicate and sell with their consumers via multiple channels. For example, they can use their e-commerce website, Facebook e-shop, email account, and Instagram account.