In the past 12 months, there has been tremendous traction and advance of A.I. across industries. Moving from a buzzword, hype, or having some novelty level, firms have moved to an actual adoption, and more importantly, tangible business results. Organizations are now breaking through the struggle to implement projects that deliver business value and are well on their journey to become sentient enterprises. The examples and use cases are astounding in number. A great indicator is the number of start-ups driving radical innovations, for instance, in customer experience at scale.

Artificial Intelligence is everywhere, opportunities are in abundance for cognitive enterprises. What do we mean by cognitive enterprises? Millions of ideas and think pieces are waiting to grow luxuriantly and cognitive AI technologies will play a bigger role in turning your ideas into a live piece of work. It is expected that AI will bring simplicity to complex business issues and deliver more useful, engaging, intuitive, and profitable solutions, and this is what we say a cognitive approach for enterprises. According to a report published by IDC a market research firm states that global spending on cognitive AI systems will reach $57.6 billion by 2021. Biggest investors in cognitive AI systems are banking, retail, and manufacturing firms.

Automation in the supply chain is nothing new. From the earliest days of industrialization through the present time, increasingly sophisticated automation technologies serve to drive new levels of efficiencies. Historically, automation has focused on physical tasks, making them more efficient. As technologies continue to advance, however, they may offer new ways for supply chain organizations to achieve efficiencies within areas of business operations where automation was previously considered to be impossible--areas such as thought and reason. At one level, cognitive technologies, also known as artificial intelligence (AI), continue the tradition of automating physical tasks that previous generations of automation technologies offered. But they can also go further, taking the concept of automation to the next level by automating jobs that we ordinarily associate with mental processing, learning and self-correction, sensing, and judgment--in other words, the very things that we consider to be higher-level "human" thought. And so human thought now exists along the spectrum of automation: from repeatable, predictable tasks that replicate physical labor to reasoning and decision-making.

A recent post on IDG Connect examines the impact Artificial Intelligence (AI) will have on the workplace. The piece, which outlines the major differences between chatbots and cognitive solutions, as well as the importance of reskilling human workers to collaborate with AI, heaps high praise on IPsoft's cognitive solution, Amelia. "Amelia has been over five… Read More The post IDG: 'Amelia is Something to be Proud Of' appeared first on IPsoft.

There's nothing new about organizations and their leaders to be fumbling around for a coherent, business-relevant strategy any time a new technology appears on the scene. We've been seeing this in recent years with the rise of digital, raising issues from defining what exactly digital is, to defining what it means to succeed. Now, such is the case with the constellation of cognitive solutions -- artificial intelligence, machine learning and so forth -- that are now starting to be embraced. With AI and cognitive computing the flavor of the month (or year), it's time to start exploring what, exactly, it can do for business growth, and how to go about achieving it. More good news: many of the bread-and-butter issues arising from previous generations of technology apply with AI as well -- starting with the most fundamental of fundamental principles: don't implement technology for technology's sake, have a business goal in mind.

There's nothing new about organizations and their leaders fumbling around for a coherent, business-relevant strategy any time a new technology appears on the scene. We've been seeing this in recent years with the rise of digital, raising issues from defining what exactly digital is, to defining what it means to succeed. Now, such is the case with the constellation of cognitive solutions -- artificial intelligence, machine learning and so forth -- that are now starting to be embraced. With AI and cognitive computing the flavor of the month (or year), it's time to start exploring what, exactly, it can do for business growth, and how to go about achieving it. More good news: many of the bread-and-butter issues arising from previous generations of technology apply with AI as well -- starting with the most fundamental of fundamental principles: don't implement technology for technology's sake, have a business goal in mind.

This is the time of year when everyone looks to the year ahead. Here are five four things in data science and machine learning that are utterly and completely predictable in 2018. In the Pleistocene Era of Data Science, there were Heroes and Hackers: lone souls working on ad hoc projects with Pig, Hive, Mahout, Java, and a few prayers. For asset management, organizations used thumb drives and email. Collaboration was a non-issue because there were few others, if any, to collaborate with.

Cognitive technology solutions that leverage artificial intelligence (AI) advancements have moved seemingly overnight from the computing fringe to mainstream business practice. At least, that's what a recent survey conducted by IBM and IDG suggests at first glance. Of the 200 executives and decision makers at large enterprises who participated, 51% said they have already deployed AI in their cloud environments, and the remaining 49% said they would do so in the next 12 months. But what types of cognitive solutions are being adopted? The IBM/IDG survey provides data on two high-profile AI-based technologies: Nearly two-thirds (64%) of the survey respondents said they have already deployed machine learning technology in some fashion, and 56% said they are currently using "bots" – software-based robots that automate tasks and human-machine interactions in various ways.

October 24, 2017 Written by: Chitra Dorai, Ph.D. Welcome to the era of cognitive computing. It's an era of artificial intelligence--systems that gather information, analyze, recommend, plan and more importantly, learn . In short, they help you make better decisions. Today's data volumes are enormous and rich in variety --and now deep learning systems can ingest, analyze and reason across that data in all its forms. These cognitive systems are a huge leap forward leading to a "rethink" of the way people live, engage, and work.

Cognitive applications have become constant companions at our places of work. We expect smart systems to reduce repetitive workloads and support us in uncovering new Knowledge. As a result, data scientists and software engineers are applying various machine learning algorithms to finetune results and increase processing capabilities. At the same time, critics are ever more loudly calling for more transparency about how these cognitive applications actually function. Companies are also advised to not to manage their AI-driven application environment solely on technical grounds.