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…
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…
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!
It is estimated that in 2025 $43289.9 US Dollar revenue would be generated globally with the use of NLP worldwide.
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. …
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.
American Express? A financial services provider was facing challenges in customer retention. Managing the customer churn was crucial to them. So, in 2017, they experimented with predictive analytics using machine learning and developed a forecasting model for potential churn. It is believed that the model identified 24% of Australian accounts that will close within the next four months!
In a data-driven world, data analytics & Machine Learning in the banking & financial services sector has the potential to play a crucial role. …
Artificial intelligence (AI) uses personified knowledge and learns from the solutions it produces to address not only specific but also complex problems.
At present, the pharmaceutical industry is facing challenges in sustaining its drug development programs because of increased R&D costs and reduced efficiency.
The major issue arises from the essence that machine learning algorithms were not primarily developed to deal with eloquent and dynamic adversaries, and therefore, in concept, the entire security level can be breached by leveraging the relevant security issues of mastering algorithms through meticulous tampering of raw data.
Here Adversarial learning can come into the picture!!
Adversarial learning is a novel research area that mainly lies in the convergence of machine learning and cybersecurity. It focuses on allowing the secure implementation of machine learning approaches in adverse circumstances such as spam filtering data protection and biometric identification.
Adversarial machine learning…
The Cloud Native Computing Foundation (CNCF) found that in 2019 the vast majority of respondents — 84 percent — were running containers in production. That was up roughly 15 percent (or 11 percentage points, from 73) from the previous year. Production container usage was at just 23 percent when CNCF first did its survey in March 2016.
As a newbie in “Machine Learning”, it’s always an exciting journey that starts from data cleaning, exploratory data analysis, feature engineering, and it usually ends at model training and deploying it through the flask. …
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