The 10 Best Professional Services Automation Solution providers 2018
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About the Author<br />
Mr. Jay Klein drives Voyager Lab’s technology<br />
strategy and core intellectual property. He brings<br />
more than 25 years of experience in data analytics,<br />
networking and telecommunications to the Company.<br />
Before joining Voyager Labs, he served as CTO at<br />
Allot Communications where he steered Allot’s data<br />
inspection and analytics core technology offerings,<br />
and as VP Strategic Business Development at DSPG,<br />
where he was responsible for strategic technology<br />
acquisitions. He also co-founded and held the CTO<br />
position at Ensemble Communications while founding<br />
and creating WiMAX and IEEE 802.16. He also<br />
served as the CTO and VP of R&D at CTP Systems,<br />
acquired rst by DSP Communications and later by<br />
Intel. Jay Klein holds a BSc in Electronics & Electrical<br />
Engineering from Tel Aviv University as well as<br />
numerous patents in various technology elds.<br />
With so much attention focused on Artificial<br />
Intelligence (AI), it’s worth remembering that<br />
one size does not fit all. <strong>The</strong>re are specific<br />
business-related pain points in mind when a company<br />
decides to deploy AI technology, so making the right<br />
choices can be a tricky task.<br />
For example, several months ago, an AI related<br />
breakthrough was announced – a robot learned and<br />
demonstrated the ability to perform a perfect backflip.<br />
While it is well acknowledged that the invested research<br />
and development for this mission was huge and the<br />
commercial potential for some applications is enormous, it<br />
is somewhat unclear how this specific innovation or the<br />
core models and algorithms of it, can serve other industries<br />
and verticals. Herein lies the problem.<br />
AI: From<br />
Artificial<br />
to Authentic<br />
Gauging AI success in one field in many cases can be<br />
meaningless for another. To make things worse, even when<br />
trying to go deeper into the technology and attempting to<br />
evaluate, for example, which Machine Learning algorithms<br />
are utilized by the product, or what are the number of<br />
layers in the Deep Neural Network models mentioned by<br />
specific vendors, in the end it will be possibly pointless as<br />
it does not directly reflect the solution deployment<br />
‘success’ implications.<br />
Nevertheless, it seems that the market ignores this reality<br />
and continues to evaluate AI-based products by buzzword<br />
checklists using familiar and related AI terminology (e.g.,<br />
44<br />
|November <strong>2018</strong>|