AI & Machine Learning

The rise of AI taught us that humans are nothing but another input and output. Words and emotions are the programming language and the machines can program us with ease. We once imagined we were the only masters of our destiny and the sole captain of our ships. Our minds were made up by our own free will. But we know it’s not true now. As we untangled the inner mysteries of our minds’ complex electrochemical field we realized we were just another kind of artificial intelligence, one evolved through the great genetic algorithms of the Earth’s biome.

Artificial intelligence (AI) is poised to tip new enterprise collaboration in a positive direction as it takes a more significant role in the platform search functionality and UX. The introduction of AI to enterprise collaboration means that your content can meet users where they are. It takes out the poor UX and the need for human intervention to track down needed documents on deadlines. Here’s how the role of AI will play out in the next generation of enterprise collaboration.

No two users see the same news feed in their social media accounts and their news curation apps. AI-driven news curation makes sure you see “relevant” results, as the curators will tell you. But in effect, these algorithms submerge you in a filter bubble, where you see too much of what you want to see and too little of what you should see. The direct result of filter bubbles is polarization of societies, amplification of biases, less tolerance for opposing views, and more vulnerability to fake news.

AI created an explosion of new jobs the likes of which the world had never seen. First came the destruction but then came the creation. New jobs we couldn’t imagine before the old ones disappeared started to flourish. People worked side by side with AI, as politicians and big companies and decentralized autonomous organizations (DAOs) learned how to balance the needs of man and machine. In just fifty years we saw the mustard seeds of the dawn of the Age of Intelligence grow into a wild and uncontrolled forest.

Artificial Intelligence (AI) automates analytical processes much like robots and production machinery automate physical processes for companies. The misconception is robots are the front line of AI. That is simply not the case anymore especially if you expand your definition to include predictive analytics. Yes, robots involve some AI. There are many uses of AI in business processes, and in the office of distributors and manufacturers. Here are a few examples.

AI is already radically changing the world. In the short term, the promise and peril of AI is legion. AI will deliver some of our brightest fantasies and our darkest nightmares. Why both? Because AI is a universal technology. It’s flexible enough to do whatever we want it to do. And that means it will reflect the good and evil of its creators: Us. Let’s dive in and take a look at how AI will change society in the next few years, and by the time you’re old and grey, and when you’re long since turned to dust.

Machine learning (ML) and artificial intelligence (AI) applications are booming in the corporation world.  While algorithms aren’t always replacing humans, they are usually changing the way we work. No leader is safe from the rapid change we are seeing in the age of algorithms.  Businesses are transforming at rapid pace and there is no time to dilly dally. Learn how to make use of algorithms to build on your human skills or risk being replaced entirely.

The mechanism that drives smart farming is Machine Learning — the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data intensive processes in agricultural operational environments. Machine learning is everywhere throughout the whole growing and harvesting cycle. Let’s discover how agriculture can benefit from Machine Learning at every stage.

Spark NLP library is the 7th most popular across all AI frameworks and tools. It is also by far the most widely used NLP library – twice as common as spaCy. Optimizations are done to get Apache Spark’s performance closer to bare metal, on both a single machine and cluster, meant that common NLP pipelines could run orders of magnitude faster than what the inherent design limitations of legacy libraries allowed. Being natively built on Apache Spark ML enables Spark NLP to scale on any Spark cluster, on-premise or in any cloud provider. 

Integration of computer vision and natural language processing (NLP) is the most actively developing machine learning research areas.  Yet, until recently, they have been treated as separate areas without many ways to benefit from each other. Since the integration of vision and language is a fundamentally cognitive problem, research in this field should take account of cognitive sciences that may provide insights into how humans process visual and textual content as a whole and create stories based on them.

If you look out at the world of platform companies, you will quickly find that use of AI for curation is a hallmark of the outperforming platform.  If your organization wants to enter adopt a platform strategy and begin taking advantage of the networks effects it offers, you had better recognize that curation is an essential part of the journey and make sure you have the machine learning competency needed to make it happen.