With the increasing use of cloud-based services for training and deploying machine learning models, data privacy has become a major concern. This is particularly important for natural language processing (NLP) models, which often process sensitive information such as personal communications and confidential documents. In this study, we propose a method for training NLP models on encrypted text data to mitigate data privacy concerns while maintaining similar performance to models trained on non-encrypted data. We demonstrate our method using two different architectures, namely Doc2Vec+XGBoost and Doc2Vec+LSTM, and evaluate the models on the 20 Newsgroups dataset. Our results indicate that both encrypted and non-encrypted models achieve comparable performance, suggesting that our encryption method is effective in preserving data privacy without sacrificing model accuracy. In order to replicate our experiments, we have provided a Colab notebook at the following address: https://t.ly/lR-TP

Artificial intelligence (AI) and machine learning (ML) are some of the most hyped enterprise technologies and have caught the imagination of boards, with the promise of efficiencies and lower costs, and the public, with developments such as self-driving cars and autonomous quadcopter air taxis. Of course, the reality is rather more prosaic, with firms looking to AI to automate areas such as online product recommendations or spotting defects on production lines. Organisations are using AI in vertical industries, such as financial services, retail and energy, where applications include fraud prevention and analysing business performance for loans, demand prediction for seasonal products and crunching through vast amounts of data to optimise energy grids. All this falls short of the idea of AI as an intelligent machine along the lines of 2001: A Space Odyssey's HAL. But it is still a fast-growing market, driven by businesses trying to drive more value from their data, and automate business intelligence and analytics to improve decision-making. Industry analyst firm Gartner, for example, predicts that the global market for AI software will reach US$62bn this year, with the fastest growth coming from knowledge management.

Microsoft debuted a slew of new AI technologies at their annual Ignite conference. One of the most interesting is an AI system called "PeopleLens." PeopleLens is a platform that uses computer vision algorithms to help blind people engage with their social surroundings. The system is designed to identify and interpret objects in the user's environment and relay those details back to the user in a way that they can understand. This opens a world of possibilities for blind people, who until now have been largely cut off from social interaction. With PeopleLens, they can now participate in conversations, navigate their surroundings, and generally experience the world in a way that was once impossible.

Working on machine learning and AI may seem futuristic and need of today's world. Machine learning is taking the present teach industry by storm as it has become a prerequisite for the apps. It has several advantages and saves time in the long run. In this article, we'll cover all that you need to know on how to add machine learning to your app. There are so many ways you can implement machine learning and AI behaviors into apps to get the most out of your work.

Emerging data collection technologies are giving businesses and society the power to understand the present and fuel the future. Whether it's the ability to reduce air pollution by measuring traffic patterns, tracking a supply chain between companies and across borders, or tapping into intelligent systems to unlock actionable insights and personal benefits, data impacts every aspect of our socio-economic and personal lives through the lens of emerging technologies like IoT, Blockchain, and more, we'll examine how data is becoming the world's most valuable asset, making innovation possible and transforming the way we make decisions for the future. Within months, what we consider an emerging technology can change due to the rapid nature itself. These technologies vary wildly, with some crossing over and linking technologies together. Artificial Intelligence (AI) has come into everyday life.

There are many sources that give similar answers to the question, 'What is AI?' By the 1950s, there were many scientists, mathematicians, and philosophers that were looking into the concept of Artificial Intelligence. One such person was Alan Turing, who to this day is considered by many to be the Father of Artificial Intelligence. He formed the idea and mathematical and logical reasoning behind the concept of machine intelligence wherein machines and computers would be able to replicate the behavior of humans and their intelligence. His paper Computing Machinery and Intelligence outlines his logic for the start of artificial intelligence. Fast forward 70 years into the future and we are now in a world where computers are able to converse with humans, albeit with limitations, but this is the progress we see as our world progresses to a more sophisticated AI. "The design and development of computer systems that have the knowledge and skills required to perform the tasks which usually require human intelligence to undertake" –AILab "The ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings."

This year "is the year that AI is going to enter the enterprise mainstream adoption," said Jeff Loucks, executive director of The Center for Technology, Media & Telecommunications at Deloitte Services LP. Deloitte's 2020 edition of its annual "State of AI in the Enterprise" report, released in July, indicates that many enterprises are investing heavily in AI, and many are buying cloud-based AI products instead of building their own. The technology and consulting company surveyed 2,737 IT and line-of-business executives across nine countries. All of the respondents use some form of AI in their companies. The survey showed that 53% of the adopters spent more than $20 million over the past year on AI-related technology and talent, with 71% of them expecting to increase spending in the next fiscal year.

With growing attention devoted to AI, machine learning, and IoT, what we've come to know as big data has become an even broader version of itself. In recent years, big data was seen as an unstoppable force of nature that would either overwhelm enterprises or propel them to new heights. This next generation of big data -- we'll call it expansive data, pulsing through systems in real-time, powering processes unseen to human eyes, and adapting and learning as it goes along -- is going to reshape enterprises in ways not even anticipated. This requires attention to new types of tools, platforms, and approaches to deliver value to today's data-hungry businesses. Expansive data will represent ever-growing volumes of information, potentially increasing within enterprises at a rate of up to 36% a year, according to Dresner Advisory Services.

AI, Machine Learning, IoT and Cloud-Based Services Must Deliver Value From Their Data. With growing attention devoted to AI, machine learning, and IoT, what we've come to know as big data has become an even broader version of itself. In recent years, big data was seen as an unstoppable force of nature that would either overwhelm enterprises or propel them to new heights. This next generation of big data -- we'll call it expansive data, pulsing through systems in real-time, powering processes unseen to human eyes, and adapting and learning as it goes along -- is going to reshape enterprises in ways not even anticipated. This requires attention to new types of tools, platforms, and approaches to deliver value to today's data-hungry businesses.

Businesses that use artificial intelligence (AI) and connected technology to reveal new insights "will steal $1.2 trillion each year from their less-informed peers by 2020." Although AI has been around since the Fifties, it's solely recently that the technology has begun to seek out real-world applications (such as Apple's Siri). The investment in AI by each tech giants also as start-ups has enhanced three folds to $40 Billion as of 2017. Improvement in machine learning (ML) algorithms--due to the supply of enormous amounts of information Greater computing power and also the rise of cloud-based services--which helps run refined machine learning algorithms. Greater computing power and also the rise of cloud-based services--which helps run refined machine learning algorithms.