- SAP Managed Tags:
Artificial intelligence (AI) has earned significant buzz over the last year. Across a wide range of sectors and geographies, established conglomerates, emerging startups, and everyone in between are focusing on the potential disruption looming ahead and innovating to embrace game-changing opportunity and efficiency. According to IDC, mass adoption of cognitive systems and AI will continue to boost worldwide revenues with a compound annual growth rate of 55.1% – from nearly $8 billion in 2016 to over $47 billion in 2020.
In a few short years, AI has become the “brains” behind many recent breakthroughs. From helping doctors identify disease early-on to making cars more autonomous and matching travelers to the accommodations they want, this technology is quickly changing our world. Unfortunately though, many executives still do not have a clear view of the value it offers.
CIOs inherently know the risk of waiting a few years until the C-suite gets on board. How can they invest anything in AI solutions if the boardroom doesn’t have a clear sense of what it wants to accomplish?
As more companies begin to test the technology and bring it to the masses, this indecisive stalemate warrants thought-provoking discussion about the future of the business. But instead of evangelizing the advantages of AI as a whole, it is best to break it down into two distinct forms: machine learning and deep learning.
Machine learning is a process that allows computers to improve take performance through increased exposure to data. Rather than using traditional programming methods, this technique requires a high degree of computational power to sift through massive volumes of information. Over time, machines can better sense, perceive, learn, and respond to their environment and their users through manual selection of relevant learning – enabling everything from better service to transforming how customers and companies engage with each other.
The opportunity: The task of machine learning is traditionally relegated to clusters of energy-guzzling computer servers equipped with specialized processors. However, as mobile devices continue to grow in computing power and perform tasks offline, the greater capacity for machine learning to become a mainstream activity that happens anytime and anywhere. For a growing remote workforce, this latest development is great news. For example, healthcare providers in isolated villages can diagnose skin conditions by analyzing digital photos without requiring connectivity access.
As an advanced branch of AI with predictive capabilities, deep learning mirrors the human brain’s capacity to continuously learn with higher accuracy and faster processing than traditional computing models. This approach is resistant to small changes and obstructions and can automatically make general assumptions from partial data. Fundamentally, deep learning can evaluate an object, digest the information, and adapt as conditions change.
The opportunity: Although it is not as mature as machine learning, deep learning is ripening to become a significant investment opportunity. This AI form leverages other technologies – such as in-memory computing, online presence, predictive analytics, the Internet of Things, and cloud solutions – to resolve increasingly complex problems, once thought impossible, across all types of industries.
Breakthroughs in deep learning are now starting to teach machines to process natural language and interpret the information into decision and action. Innovations from startups, such as Kore, has taken this technology to a new level by making devices smarter and able to evolve with the needs of the user.
Even though IT budgets continue to shrink, the influencing power of the CIO shouldn’t. Line-of-business leaders will eventually invest in AI directly and search for third-party vendors to implement it quickly. By educating executives on the advantages of each form of AI, CIOs can help ensure that every dollar spent permeates value throughout the enterprise.
Learn more about artificial intelligence in our Thursday series blogs, which cover all things machine learning and predictive analytics.
This post originally appeared in the SAP D!gitalist Magazine and has been republished with permission.
In a few short years, AI has become the “brains” behind many recent breakthroughs. From helping doctors identify disease early-on to making cars more autonomous and matching travelers to the accommodations they want, this technology is quickly changing our world. Unfortunately though, many executives still do not have a clear view of the value it offers.
CIOs inherently know the risk of waiting a few years until the C-suite gets on board. How can they invest anything in AI solutions if the boardroom doesn’t have a clear sense of what it wants to accomplish?
As more companies begin to test the technology and bring it to the masses, this indecisive stalemate warrants thought-provoking discussion about the future of the business. But instead of evangelizing the advantages of AI as a whole, it is best to break it down into two distinct forms: machine learning and deep learning.
Machine Learning: The Core of Intelligence
Machine learning is a process that allows computers to improve take performance through increased exposure to data. Rather than using traditional programming methods, this technique requires a high degree of computational power to sift through massive volumes of information. Over time, machines can better sense, perceive, learn, and respond to their environment and their users through manual selection of relevant learning – enabling everything from better service to transforming how customers and companies engage with each other.
The opportunity: The task of machine learning is traditionally relegated to clusters of energy-guzzling computer servers equipped with specialized processors. However, as mobile devices continue to grow in computing power and perform tasks offline, the greater capacity for machine learning to become a mainstream activity that happens anytime and anywhere. For a growing remote workforce, this latest development is great news. For example, healthcare providers in isolated villages can diagnose skin conditions by analyzing digital photos without requiring connectivity access.
Deep Learning: Filling in the Edges with a Mind of Its Own
As an advanced branch of AI with predictive capabilities, deep learning mirrors the human brain’s capacity to continuously learn with higher accuracy and faster processing than traditional computing models. This approach is resistant to small changes and obstructions and can automatically make general assumptions from partial data. Fundamentally, deep learning can evaluate an object, digest the information, and adapt as conditions change.The opportunity: Although it is not as mature as machine learning, deep learning is ripening to become a significant investment opportunity. This AI form leverages other technologies – such as in-memory computing, online presence, predictive analytics, the Internet of Things, and cloud solutions – to resolve increasingly complex problems, once thought impossible, across all types of industries.
Breakthroughs in deep learning are now starting to teach machines to process natural language and interpret the information into decision and action. Innovations from startups, such as Kore, has taken this technology to a new level by making devices smarter and able to evolve with the needs of the user.
Evangelizing the C-Suite on the Power of AI
Even though IT budgets continue to shrink, the influencing power of the CIO shouldn’t. Line-of-business leaders will eventually invest in AI directly and search for third-party vendors to implement it quickly. By educating executives on the advantages of each form of AI, CIOs can help ensure that every dollar spent permeates value throughout the enterprise.
Learn more about artificial intelligence in our Thursday series blogs, which cover all things machine learning and predictive analytics.
This post originally appeared in the SAP D!gitalist Magazine and has been republished with permission.
