Speech recognition, image recognition, analysis of behavioral pattern (e.g. for fraud detection), targeted adverts, recommendation for shopping, algorithmic trading are few examples of applied AI. In recent time, we have noticed AI boom primarily led by the internet giants; Google, Facebook, Amazon, Microsoft, Baidu and others. Recent resurgence of startups is seen in the AI space. AI and Automation featured prominently on Gartner's hype cycle 2015 with Autonomous Vehicles put at the "Peak of Inflated Expectations". Garner also defined "Autonomous" as the sixth and final stage of an organization's journey towards being a truly "digital enterprise". It highlighted a range of technologies as emerging technologies.

Artificial Intelligence (AI) has often been popularly envisaged in super-smart humanoid robot form. In fact, it's more commonly implemented as behind-the-scenes algorithms that can process'big' data to accomplish a range of relatively mundane tasks far more efficiently than humans can. Few of us, yet, interact with bipedal robots or take a ride in a driverless car, but our daily lives are increasingly affected by AI systems that can recognise speech or images, or analyse patterns of online behaviour (to detect credit card fraud or serve up appropriate adverts, for example). Nor is it any surprise to find AI and automation featuring prominently on analyst firm Gartner's 2015 Hype Cycle for Emerging Technologies, which places autonomous vehicles, for example, right at the hype-driven'Peak of Inflated Expectations': Gartner also identifies'Autonomous' as the sixth and final stage of an organisation's journey to becoming a'digital business', highlighting a range of emerging technologies as particularly relevant: Autonomous Vehicles, Bioacoustic Sensing, Biochips, Brain-Computer Interface, Digital Dexterity, Human Augmentation, Machine Learning, Neurobusiness, People-Literate Technology, Quantum Computing, Smart Advisors, Smart Dust, Smart Robots, Virtual Personal Assistants, Virtual Reality, and Volumetric and Holographic Displays. What follows hype, of course, is disillusionment: this has happened to AI before, and it's unlikely that all of the technologies flagged up in Gartner's Hype Cycle will make it to the mainstream.

Often, Data Science for IoT differs from conventional data science due to the presence of hardware. Hardware could be involved in integration with the Cloud or Processing at the Edge (which Cisco and others have called Fog Computing). Hardware will increasingly play an important role in Data Science for IoT. In IoT, speed and real time response play a key role. Often it makes sense to process the data closer to the sensor.

A modular analogue neuro-chip set with on-chip learning capability is developed for active noise canceling. The analogue neuro-chip set incorporates the error backpropagation learning rule for practical applications, and allows pinto-pin interconnections for multi-chip boards. The developed neuro-board demonstrated active noise canceling without any digital signal processor. Multi-path fading of acoustic channels, random noise, and nonlinear distortion of the loud speaker are compensated by the adaptive learning circuits of the neuro-chips. Experimental results are reported for cancellation of car noise in real time.

A modular analogue neuro-chip set with on-chip learning capability is developed for active noise canceling. The analogue neuro-chip set incorporates the error backpropagation learning rule for practical applications, and allows pinto-pin interconnections for multi-chip boards. The developed neuro-board demonstrated active noise canceling without any digital signal processor. Multi-path fading of acoustic channels, random noise, and nonlinear distortion of the loud speaker are compensated by the adaptive learning circuits of the neuro-chips. Experimental results are reported for cancellation of car noise in real time.

A modular analogue neuro-chip set with on-chip learning capability is developed for active noise canceling. The analogue neuro-chip set incorporates the error backpropagation learning rule for practical applications, and allows pinto-pin interconnections for multi-chip boards. The developed neuro-board demonstrated active noise canceling without any digital signal processor. Multi-path fading of acoustic channels, random noise, and nonlinear distortion of the loud speaker are compensated by the adaptive learning circuits of the neuro-chips. Experimental results are reported for cancellation of car noise in real time.

Electronic synapses based on CMOS, EEPROM, as well as thin film technologies are actively being developed [1-5]. One preferred approach is based on a hybrid digital-analog design which can easily be implemented in CMOS with simple interface and analog circuitry. The hybrid design utilizes digital memories to store the synaptic weights and digital-to-analog converters to perform analog multiplication. A variety of synaptiC chips based on such hybrid designs have been developed and used as "building blocks" in larger neural network hardware systems fabricated at JPL. In this paper, the design and operational characteristics of the hybrid synapse chips are described.

The hybrid design utilizes digital memories to store the synaptic weights and digital-to-analog converters to perform analog multiplication.

Electronic synapses based on CMOS, EEPROM, as well as thin film technologies are actively being developed [1-5]. One preferred approach is based on a hybrid digital-analog design which can easily be implemented in CMOS with simple interface and analog circuitry. The hybrid design utilizes digital memories to store the synaptic weights and digital-to-analog converters to perform analog multiplication. A variety of synaptiC chips based on such hybrid designs have been developed and used as "building blocks" in larger neural network hardware systems fabricated at JPL. In this paper, the design and operational characteristics of the hybrid synapse chips are described.