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“Netflix has a global algorithm which is helpful in the recommendations to all the users and the company claims that the combined effect of personalization and recommendations is worth $1 Billion per year!”

Well, the above statistics are not much surprising as we know what potential the recommendation systems are having these days! More surprising are, the use-cases of Artificial Intelligence (AI)!

AI has paved its way into the film-making industry now!

Recently, a group of researchers presented their research in “The Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing” forming a deep learning model to analyze and predict the movie ratings based on the language data in the movie script! …

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Source : Towards Data Science

According to David Kenny, General Manager, IBM Watson — the most advanced cognitive computing framework, “AI can only be as smart as the people teaching it.” The same is not true for the latest cognitive revolution.

What is Cognitive Computing?

Cognitive computing is the use of computerized models to simulate the human thought process in complex situations where the answers may be ambiguous and uncertain. The phrase is closely associated with IBM’s cognitive computer system, Watson. Cognitive computing overlaps with AI and involves many of the same underlying technologies to power cognitive applications, including expert systems, neural networks, robotics and virtual reality (VR).

How Cognitive Computing works?

Cognitive computing systems can synthesize data from various information sources, while weighing context and conflicting evidence to suggest the best possible answers. To achieve this, cognitive systems include self-learning technologies that use data mining, pattern recognition and natural language processing (NLP) to mimic the way the human brain works.

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Source: Adext

Google and Facebook combined hold 84% of the market share in global digital advertising according to the Financial Times in 2017. Among the leaders in this field is Adext AI, whose audience monitoring system can improve ad investment efficiency by as much as +83% % in just ten days.

What is Adext AI?

Adext AI, a framework that streamlines electronic advertisement and promotional operations using AI, prevents services from hours spent on operational and routine activities.

In today's modern environment, Adext is the first and yet the only Ad Tech Artificial Intelligence (AI) Technology that serves as a Software as a Service (SaaS).

Once subscribers just register in or log in with their credentials and link their account to Google Adwords, Facebook Ads, doing some of the requisite configurations, then would like to see the capabilities of Ai. As the numbers suggest, this site is rising transactions by more than 500 percent.

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Source: Amazon

“Market analysts predict that more than 750 million Edge AI chips and computers will be sold in 2020, rising to 1.5 billion by 2024. And while most of these will be installed in consumer devices like phones”

To those who are following, the advancements in Machine Learning (ML)/Artificial Intelligence (AI) would be surely aware of the fact that the usage of these technologies would be predominant in the upcoming future!

While parallel to that growth, even the Internet of Things (IoT) market is also growing at a full-fledged pace, with the number of IoT devices being projected in 2025 is around 21.5 …

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Source: IoT Agenda- TechTarget

According to a recent report by Transparency Market Research (TMR), the edge analytics market is foreseen to project a strong growth with a noticeable CAGR (Compound Annual Growth Rate) of 27.6% within the forecast period.

What is Edge Analytics?

Edge analytics is the advanced data analysis method that enables users to get access to real-time processing and extracting the unstructured data captured and stored on the edge of network devices. Edge analytics provides the automatic analytical computation of generated data in a real-time mode without sending the data back to the centralized data store or server.

In this technique, data is collected, processed, and analyzed at the sensor, device, or touchpoint itself. …

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Source: C#Corner

The origins of MLOps goes back to 2015 from a paper entitled “Hidden Technical Debt in Machine Learning Systems.” And since then, the growth has been particularly strong. Consider that the market for MLOps solutions is expected to reach $4 billion by 2025.

What is MLOps?

MLOps is intelligence that provides a bridge between data scientists and the production team. It deeply conspires in nature and designed to eliminate all the waste and make the machine learning system more scalable by providing automation and producing highly consistent insights from the ML model.

MLOps is the idea of combining the long-established practice of DevOps with the emerging field of Machine Learning. It is the creation of an automated environment for model development, model retraining, drift monitoring, automation of pipeline, quality control, and governance of a model into a single platform.

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Source: Google AI blog

Google Gboard App’s next-word prediction accuracy increased by 24% with the use of Federated Learning!

What is Federated Learning?

Lots and lots of data is being generated today on mobile phones and IoT devices (also termed as “data on edge devices”). This data provides benefits such as low-latency, privacy, and offline availability for many actions. However,

when it comes to Machine Learning(ML) and Analytics on data distributed across those devices, the usual practice for training ML models is to collect that data across different cloud or local storages at one central machine or repository. But:

  • How to regulate data sharing policies while gathering the data from heterogeneous locations? …

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“The field of exploratory data analysis was established with Tukey’s 1977 now-classic book Exploratory Data Analysis [Tukey-1977].”

Exploratory Data Analysis (EDA) is an approach to extract the information enfolded in the data and summarize the main characteristics of the data.EDA involves looking at and describing the data set from different angles and then summarizing it.

Today, this data pre-processing step is an essential one before starting statistical modeling or machine learning engines to ensure the correctness and effectiveness of data used.

Benefits of EDA:

1) It helps to clean the garbage from the dataset
2) Helps users to understand the relationship between each of the variables. …

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Source: Analytics India Magazine

AutoML, Google’s AI that helps the company create other AIs for new projects, learned to replicate itself in October of 2017. Essentially, AI is better at ML than humans.

Automated Machine Learning is the process that involves automation of the procedure of machine learning lifecycle like Feature selection, Model Selection, Hyperparameter optimization. Above mentioned techniques will be directly implemented by a system no human effort requires.

Why it is Important?

Manually constructing a machine learning model is a multistep process that requires domain knowledge, mathematical expertise, and computer science skills. …

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Source: Pinterest

“According to a recent market report, the graph analytics market was valued at US$600 million in 2019, and it is forecast to grow US$2.5 billion by 2024”

What is Graph Analytics?

We’ve seen relational analytics being performed on various types of data which is structured & table-formatted data, and analyzing that helps us to gain underlying insights from the data but,

“Graph Analytics covers analysis of relationships between attributes of data by forming a graph-like structure with nodes, edges, and weight from the data”

It is different from relational analytics by the fact that the former tries to find insights from the data by comparing through one-to-one or one-to-many approach but graph analytics can leverage many-to-many approach & also works best on unstructured data!


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