The difference between artificial intelligence and intelligent automation


A main point of the difference between artificial intelligence and intelligent automation is that while artificial intelligence is about autonomous workers capable of mimicking human cognitive functions, intelligent automation is all about building better workers, both human and digital, by embracing and working alongside intelligent technologies. Intelligent automation enables large-scale data analysis and improved productivity. With intelligent automation, once the processes have been refined and validated, organisations can then automate back-end processes, scenarios, data capture, analysis and much more – all unsupervised and while other operations are going on. Therefore, it is vital that organisations not only include both technologies to ensure future success, but also understand how the difference between artificial intelligence and intelligent automation solutions can help them to be more effective overall – and deploy them in the right areas of the organisation.

Google Announces Tensorflow Lite: A Neural Network Library for Mobile Phones


Dave Burke, VP of engineering at Google, announced a new version of Tensorflow optimised for mobile phones. This new library, called Tensorflow Lite, would enable developers to run their artificial intelligence applications in real time on the phones of users. At the moment, most artificial intelligence processing happens on servers of software as a service providers. This framework has been used for real time style-transfer: adding art-like filters to your mobile phone.

Quantum Computing, Deep Learning, and Artificial Intelligence


How Quantum can be used to dramatically enhance and speed up not just Convolutional Neural Nets for image processing and Recurrent Neural Nets for language and speech recognition, but also the frontier applications of Generative Adversarial Neural Nets and Reinforcement Learning. While supply chain, cybersecurity, risk modeling, and complex system analysis are all important segments of data science, they don't hold nearly the promise of what a massive improvement in Deep Learning would mean commercially. Optimization problems extend beyond the realm of traditional data science to include incredibly complex problems like protein folding or test flying space craft based on mathematical models. Can We Make Quantum Computers Work Like Deep Neural Nets?

Under the hood of machine learning


It arose within the interesting confluence of emergence of big data, cheap and powerful computational processing, and more efficient data storage. For instance, the machine learning tools must integrate easily with the software platforms that support existing business processes, users, and diverse projects. IT departments find themselves orchestrating data processing tools, data stores, integration, distributed computing primitives, cluster managers and task schedulers, deployment, configuration management, data analytics, and machine learning tools. DC/OS allows enterprises to run relational databases, data warehouses, and big data platforms, and manage enterprise applications and cloud-native applications within the same platform.

In the General AI Challenge, Teams Compete for $5 Million

IEEE Spectrum Robotics Channel

Rosa recently took steps to scale up the research on general AI by founding the AI Roadmap Institute and launching the General AI Challenge. In some rounds, participants will be tasked with designing algorithms and programming AI agents. The Challenge kicked off on 15 February with a six-month "warm-up" round dedicated to building gradually learning AI agents. The tasks were specifically designed to test gradual learning potential, so they can serve as guidance for the developers.

Storytelling with Data: Our Brains Crave Structure Love Oddballs


So you can potentially activate parts of your brain involved in motor control or your sense of touch. When creating your own stories, remember that the brain craves structure and loves oddballs. The brain processes information by taking information it already knows to infer what a new piece of information might be. Now that you have some basic understanding of brain anatomy and neuroscience, try applying the lessons learned to your data stories.

Story of Anima Anandkumar, the machine learning guru powering Amazon AI


Our Techie Tuesdays protagonist of the week, Anima has worked towards establishing a strong collaboration between academia and industry. Anima worked on solving this problem of tracking end to end service level transactions. She wanted to design learning algorithms that can process at scale and make efficient inferences about the underlying hidden information. When Anima joined UC Irvine as a faculty, that time was the beginning of the big data revolution.



"Our experience working with insurers suggests that – by using machines instead of humans – insurers could cut their claims processing times down from a number of months to just a matter of minutes. But, when it comes to the advice and advocacy provided by an experienced broker, BizCover Managing Director Michael Gottlieb isn't convinced intermediaries are an endangered species. Suncorp's latest Insurance Insights white paper suggests that the automation of individual consumer products and small business packages is affecting the way that insurance professionals are recruited. However, BizCover's Michael Gottlieb approaches the human resource debate from a different angle, reflecting a more future-focused solution.


New Scientist

To demonstrate the power of a new chip that can run artificially intelligent algorithms, researchers have put it in a doll and programmed it to recognise emotions in facial images captured by a small camera. The total cost of putting the new chip together is just €115 – an indicator of how easy it is becoming to give devices basic AI abilities. Recent advances in AI mean we already have algorithms that can recognise objects, lip-read, make basic decisions and more. "We will have wearable devices, toys, drones, small robots, and things we can't even imagine yet that will all have basic artificial intelligence," says Deniz.

75 Big Data Terms to Know to Make your Dad Proud


Algorithm, Analytics, Descriptive analytics, Prescriptive analytics, Predictive analytics, Batch processing, Cassandra, Cloud computing, Cluster computing, Dark Data, Data Lake, Data mining, Data Scientist, Distributed file system, ETL, Hadoop, In-memory computing, IOT, Machine learning, Mapreduce, NoSQL, R, Spark, Stream processing, Structured Vs. Unstructured Data, Now let's get on with at least 50 more big data terms. Apache Mahout: Mahout provides a library of pre-made algorithms for machine learning and data mining and also an environment to create more algorithms. Apache Drill, Apache Impala, Apache Spark SQL: All these provide quick and interactive SQL like interactions with Apache Hadoop data. It is about making sense of our web surfing patterns, social media interactions, our ecommerce actions (shopping carts etc.)