We are living in a very exciting era! Technology is changing so quickly and new capabilities amaze us every day. Modern computers interact with the real world with a minimal need for human intervention. Machines are processing huge amounts of images, free text, and sensory signals, and can affect the world with endless actuators. This creates enormous amounts of data that are collected, maintained and analyzed easier than ever.
We all saw the amazing presentation of Sofia, the robot celebrity , and can be amazed by the ability of Google photos  to recognize faces and objects, way better than us humans. And this is just the tip of the iceberg.
AI progresses dramatically. Machine learning, deep learning, deep reinforcement learning and big data have turned yesterday’s science fiction into today’s common reality. Computers become better than humans in more and more tasks and domains. We saw IBM’s Deep Blue beat Garry Kasparov , IBM’s Watson win in Jeopardy against human recognized masters , and just recently, Google’s AlphaGo beat a human professional in GO .
Computers dominate humans in image recognition and have become reasonably good at natural language processing and analyzing. Software algorithms have enabled new and exciting treatment methods for complex medical conditions. Recommendation engines make our internet and mobile app experiences more personal and valuable, almost transparently.
In a period of 80 years computers made such a progress. And today we really wonder what will happen next? Can machines think (as Allan Turing asked back in the 50s )? Will they have self-awareness? Can they be moral? Can machines be better authorities in approving AI? Do we still need a human being to run a Turing test?
We are living in a very interesting era. The era of AI’s Big Bang.
Not surprisingly, organizations all over the globe are recognizing the promise of AI and searching for ways to harness the available capabilities for their businesses. Many organizations produce high business value from AI, in retail, e-commerce, marketing tech, finance, logistic planning, healthcare, IoT and more.
Yet, there is still a significant gap between the promise of AI for businesses and the ability to produce value from it effectively. In order to transform AI into a business success, organizations must design business solutions that use AI, and it is a matter of engineering more than it is of science. There is a handful of challenges: focused business characterization of the required solution, collection, curation and integration of data from different sources, effective technological architecture, and maintenance of algorithms in a continuously changing environment.
The bottom line is that as it is today, we still need human intelligence to design a winning AI solution.
You may also be interested in our other posts:http://www.hansonrobotics.com/robot/sophia/  Google Fotos: https://photos.google.com/  IBM100: http://www-03.ibm.com/ibm/history/ibm100/us/en/icons/deepblue/  IBM Watson: https://www.ibm.com/watson/  Deepmind: https://deepmind.com/research/alphago/  Turning, A. (1950). Computing Machinery and Intelligence. Mind, 49, 433-460
Shahar Cohen is a co-founder at YellowRoad