A blog on machine learning (the original was linked here).
"Alexa, tell me a joke"
"Siri, what´s the weather going to be like in London tomorrow?"
"Siri, what´s the weather going to be like in London tomorrow?"
These are everyday questions, asked by humans, answered by Artificial Intelligence (AI). Siri and Alexa are automated personalities, integrated into the domestic sphere. Imagine a world, where your alarm clock tells the coffee machine to ensure a cappucino is ready by the time you reach the kitchen. A world, where cars no longer require a driver. A world, where board members are replaced by AI.
Stop imagining, these scenarios are already here. For example, in 2014 a Hong Kong venture capital firm named an AI machine as an “equal member” to its board of directors.
We´re serious.
According to Asian Review, Deep Knowledge Ventures develops “age-related disease drugs and regenerative medicine projects. It’s program, called VITAL, can make investment recommendations about life science firms by pouring over large amounts of data”. When asked about the company’s new board member, senior partner Dmitry Kaminskiy said: “The intuition of the human investors together with machine’s logic gives a perfect collaborative team. The risk of the mistake will be minimized.”
The AI board member story was mostly a humorous one played out in the media. But extend your thinking beyond the whimsical and consider autonomous vehicles, Siri or Alexa, facial recognition technologies: you’ll soon see that AI, and it’s key enabling technology of machine learning, is very serious business indeed.
First, here’s a quick explanation from TechRepublic of how machine learning and AI fit together:
The core principle [of machine learning] is that machines take data and ‘learn’ for themselves. It’s currently the most promising tool in the AI kit for businesses. ML systems can quickly apply knowledge and training from large data sets to excel at facial recognition, speech recognition, object recognition, translation, and many other tasks. Unlike hand-coding a software program with specific instructions to complete a task, ML allows a system to learn to recognize patterns on its own and make predictions
This technology has powerful implications for enterprise technology vendors – and there are clear signs that SAP is determined to lead the pack. For one thing, they’re investing. Heavily. SAP has just announced a $35 million SAP iO Fund aimed at investing in startups focused on (among other technologies) machine learning. In the words of SAP CEO Bill McDermott, “whether it is recruiting, employee approvals, payment processing, sales discounting approval, or call center management up to and including effectively using bots, we already have our machine learning solutions in place, and we're rolling them out across industry". Note we’ve added the emphasis. In Mr. McDermott’s eyes, machine learning isn’t aspirational: the milestone has been passed, and there are more ahead. Machine learning lies firmly in SAP’s future.
With $5 billion in venture capital raised by AI companies worldwide in 2016, this isn’t just buzz. If you’re not already part of the AI transformation, there’s still time and plenty of opportunities. A search for “machine learning” in the SAP careers site turns up 21 listed positions, ranging from Senior Developer, Data Scientists, Interns, Solution Specialists, and others. But there are hundreds more with machine learning listed within the job descriptions – a sign of things to come?
As the digital evolution extends its frontiers again, SAP consultants will find AI and other technologies a huge part of their future. If consultants can build on their foundations with studies in future technologies, it could propel their careers into a leadership or specialist role.
These opportunities are taking shape right now, so understanding the path of progression is crucial.
RED builds SAP careers. We find SAP talent for hiring companies, we search for SAP professionals, and we continuously monitor the SAP-related job market. The RED Salary Hub has data from thousands of SAP consultants who can tell you in real terms what sort of money can be made from a career with SAP – check it out here.
We´re serious.
According to Asian Review, Deep Knowledge Ventures develops “age-related disease drugs and regenerative medicine projects. It’s program, called VITAL, can make investment recommendations about life science firms by pouring over large amounts of data”. When asked about the company’s new board member, senior partner Dmitry Kaminskiy said: “The intuition of the human investors together with machine’s logic gives a perfect collaborative team. The risk of the mistake will be minimized.”
The AI board member story was mostly a humorous one played out in the media. But extend your thinking beyond the whimsical and consider autonomous vehicles, Siri or Alexa, facial recognition technologies: you’ll soon see that AI, and it’s key enabling technology of machine learning, is very serious business indeed.
First, here’s a quick explanation from TechRepublic of how machine learning and AI fit together:
The core principle [of machine learning] is that machines take data and ‘learn’ for themselves. It’s currently the most promising tool in the AI kit for businesses. ML systems can quickly apply knowledge and training from large data sets to excel at facial recognition, speech recognition, object recognition, translation, and many other tasks. Unlike hand-coding a software program with specific instructions to complete a task, ML allows a system to learn to recognize patterns on its own and make predictions
This technology has powerful implications for enterprise technology vendors – and there are clear signs that SAP is determined to lead the pack. For one thing, they’re investing. Heavily. SAP has just announced a $35 million SAP iO Fund aimed at investing in startups focused on (among other technologies) machine learning. In the words of SAP CEO Bill McDermott, “whether it is recruiting, employee approvals, payment processing, sales discounting approval, or call center management up to and including effectively using bots, we already have our machine learning solutions in place, and we're rolling them out across industry". Note we’ve added the emphasis. In Mr. McDermott’s eyes, machine learning isn’t aspirational: the milestone has been passed, and there are more ahead. Machine learning lies firmly in SAP’s future.
With $5 billion in venture capital raised by AI companies worldwide in 2016, this isn’t just buzz. If you’re not already part of the AI transformation, there’s still time and plenty of opportunities. A search for “machine learning” in the SAP careers site turns up 21 listed positions, ranging from Senior Developer, Data Scientists, Interns, Solution Specialists, and others. But there are hundreds more with machine learning listed within the job descriptions – a sign of things to come?
As the digital evolution extends its frontiers again, SAP consultants will find AI and other technologies a huge part of their future. If consultants can build on their foundations with studies in future technologies, it could propel their careers into a leadership or specialist role.
These opportunities are taking shape right now, so understanding the path of progression is crucial.
RED builds SAP careers. We find SAP talent for hiring companies, we search for SAP professionals, and we continuously monitor the SAP-related job market. The RED Salary Hub has data from thousands of SAP consultants who can tell you in real terms what sort of money can be made from a career with SAP – check it out here.
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