Source: Unsplash

“A research study by Everage says, 4 in 10 marketers (40%) employ machine-learning personalization, using algorithms and predictive analytics to dynamically present recommendations and experiences tailored to individual audience members”

Gone are the days when we just talked about the buzz words such as “Artificial Intelligence” & “Machine Learning”.

These days, we are using that technology knowingly or unknowingly,

Whether it’s getting recommendations from e-commerce websites or even getting an ETA of your favorite dish that you ordered from home!

Machine Learning has the potential to solve and enhance human lives by augmenting the existing technologies with a “predictive touch”


Source: Fingent.com

AIOT: HOW THE INTERSECTION OF AI AND IOT WILL DRIVE INNOVATION FOR DECADES TO COME

WHAT IS AIOT?

AIoT is any system that integrates AI technologies with IoT infrastructure, enhancing efficiency, human-machine interactions, data management, and analytics.

IoT enables devices to collect, store, and analyze big data. Device operators and field engineers typically control devices. AI enhances IoT’s existing systems, enabling them to take the next step to determine and take the appropriate action based on the analysis of the data.

By embedding AI into infrastructure components, including programs, chipsets, and edge computing, AIoT enables intelligent, connected systems to learn, self-correct and self-diagnose…


Source: Simplilearn

The marketplace for computer vision is anticipated to exceed $48.6 billion by 2022, making it a very lucrative UX technology.

What is Computer Vision?

Computer vision is a branch of computer science that aims to develop information technologies capable of processing, analyzing, and interpreting graphical input (images or videos) in the same manner that people do. The notion of computer vision is centered on training computers to analyze and comprehend images at the pixel level.

How does Computer Vision work?

Pattern recognition is the foundation of today’s computer vision algorithms. We train computers on huge amounts of visual data — computers analyze photos, identify things on them, and look…


Source: CFO

“ After the Covid-19 pandemic hit , many researches emerged suggesting the use of machine learning models to diagnose Covid-19 from medical images. But, a recent systematic review by University of Cambridge researchers suggests that as a “too flawed” way and “unsuitable” for use in patients! “

If you’re from a Data Science / ML (Machine Learning) background, you must’ve regularly heard about ML having the potential to detect “so-and-so” disease. …


Source: Analytics Insight

Everyone in the field of Artificial Intelligence knows what neural networks are. And most practitioners know the huge processing power and energy consumption needed to train pretty much any noteworthy neural network. That is to say, for the field to develop further, a new type of hardware is needed.

Some experts consider that the quantum computer is that hardware. But even though it holds great promise, quantum computing is a technology that will take many decades to develop. Physics theories are not yet mature enough to enable the development of useful and cost-efficient devices.

Neuromorphic computing, on the other hand…


actuia.com

The forecasted AI annual growth rate between 2020 and 2027 is 33.2%.

Google Cloud revealed the global launch of Vertex AI, a regulated machine learning (ML) platform that helps businesses to speed the rollout and management of artificial intelligence (AI) models, today at Google I/O. Vertex AI takes approximately 80% lesser lines of code to train a model than competing platforms, allowing data scientists and ML engineers of all experience levels to apply Machine Learning Operations (MLOps) to effectively design and operate ML projects over the entire software lifespan.

The intention of Vertex AI is to have an environmentally friendly…


Source: Smith School of Business Queen’s University

Just 2 years ago, Gartner predicted that 85% of AI projects won’t be able to be delivered. That means, out of 20 AI projects, only 3 will succeed! Scary isn’t it?

How many of you have heard managers and tech leads planning to bring Artificial Intelligence (AI) or Machine Learning (ML) into their projects? I’m pretty sure there are many, but when it comes to their knowledge of AI or ML! There comes the question!


Source:https://seofutura.com/what-is-new-with-google-bert-update/

It is estimated that in 2025 $43289.9 US Dollar revenue would be generated globally with the use of NLP worldwide.

What is BERT in Natural Language Processing?

BERT (Bidirectional Encoder Representations from Transformers) is a paper published by researchers at Google AI Language. It has caused a stir in the Machine Learning community by presenting state-of-the-art results in a wide variety of NLP tasks, including Question Answering, Natural Language Inference, and others.

BERT’s key technical innovation is applying the bidirectional training of Transformer, a popular attention model, to language modeling. …


Source: Google AI

In recent years, the AI field has made tremendous progress in developing AI systems that can learn from massive amounts of carefully labeled data.

This paradigm of supervised learning has a proven track record for training specialist models that perform extremely well on the task they were trained to do. Unfortunately, there’s a limit to how far the field of AI can go with supervised learning alone.

Supervised learning is a bottleneck for building more intelligent generalist models that can do multiple tasks and acquire new skills without massive amounts of labeled data. Practically speaking, it’s impossible to label everything…


Time series analytics is a statistical method that interacts with time series information or pattern recognition. Time series information means the information is usable in a variety of discrete cycles or periods.

Time Series Data: The values a variable uses at various periods are observed.

Assume you had to examine a one-year time series of regular closing market values for a specific share You will receive a list of all the daily closing for the stock on each day during the previous year and organize them in sequential sequence. This is the stock’s one-year regular closing price period series.

Forecasting of Time Series Data:

The…

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