Talend senior product manager David Talaga spoke to the Computer Weekly Developer Network this month to talk about data, programming, computer vision systems, advances in AI and garden weeds. Using the now well-seasoned example of how computer vision systems learn, Talaga reminded us that a human infant might typically only need to see four or five dogs to be able to recognise a dog in the future. But, as we know, training a computer to recognise a dog in an image – and eradicate false positives (computers can mistake a dog for a fox, or a coyote, a wolf, a dingo or a jackal) – is likely to require large data sets of hundreds of thousands of images. He thinks that no matter how much technology improves, we're unlikely to ever be able to train a computer vision system in the same way as a human baby because of the unacceptable margin for error which would be present. Talaga says that when training machine systems, data integrity is key.

As every good competent and loquacious speaker knows, no true gift of the gab is ever reasonably achieved without first visiting the Irish city of Cork to kiss the Blarney Stone. Digital transformation - the buzzword that we can't get away from. Enterprises need to accelerate their digital transformation journeys to avoid being left behind in an increasingly digital world. It's not an easy feat, but one that can be costly to get wrong. Join us as Computer Weekly takes a look at how businesses can stay on track through collaboration, innovation, and listening to user needs.

As a result, transparency has become a technical issue that companies are grappling with during development. So, what do developers need to consider when it comes to building transparency into AI systems? The biggest issues regarding transparency tend to arise through implementation of statistical methods of data analysis or machine learning. Machine Learning is a powerful technique in the developer's toolkit, allowing algorithms to be designed that learn the solutions to problems from data, rather than being explicitly programmed. Unfortunately, most Machine Learning techniques are black box by nature. Take Convolutional Neural Networks (CNN), for example: this popular deep learning technique – often used for image classification tasks – relies on a large network of weighted nodes.

Bonsai has now worked with Siemens to deploy AI on a real-world machine in a test environment. Using Bonsai's AI Platform, Siemens subject matter experts trained an AI model to auto-calibrate a Computer Numerical Control (CNC) machine more than 30x faster than an expert human operator. For a fun'what is' video to learn more about Computer Numerical Control click here. CNC machines, or computer-controlled machine tools, have revolutionised manufacturing since their inception in the 1940s. However, the value that CNC machines provide global manufacturers is constrained by high maintenance costs.

Ground level definitions out of the way, what has Databricks been doing to add to unified utopia? The company has this month announced Apache Spark open-source cluster-computing framework. This means that the company is the vendor to support Apache Spark 2.3 within a compute engine, Databricks Runtime 4.0, which is now generally available. In addition to support for Spark 2.3, Databricks Runtime 4.0 introduces new features including Machine Learning Model Export to simplify production deployments and performance optimizations. "The community continues to expand on Apache Spark's role as a unified analytics engine for big data and AI. This is a major milestone to introduce the continuous processing mode of Structured Streaming with millisecond low-latency, as well as other features across the project," said Matei Zaharia, creator of Apache Spark and chief technologist and co-founder of Databricks.

First there was Artificial Intelligence (AI), then came machine learning... neural networks and finally cognitive computing technology. But then came multilingual cognitive computing technology. Cogito Studio is a product for developing customised semantic applications for text analytics, including information analysis, categorisation and extraction. Developed by Expert System in the US state of Maryland, Cogito Studio combines a cocktail of AI algorithms for simulating the human ability to read and understand language (semantics) and deep learning techniques (machine learning) to help optimise the creation of applications that are advanced, intelligent and intuitive.