Two years ago, a small company in London called DeepMind uploaded their pioneering paper "Playing Atari with Deep Reinforcement Learning" to Arxiv. In this paper they demonstrated how a computer learned to play Atari 2600 video games by observing just the screen pixels and receiving a reward when the game score increased. The result was remarkable, because the games and the goals in every game were very different and designed to be challenging for humans. The same model architecture, without any change, was used to learn seven different games, and in three of them the algorithm performed even better than a human! It has been hailed since then as the first step towards general artificial intelligence – an AI that can survive in a variety of environments, instead of being confined to strict realms such as playing chess. No wonder DeepMind was immediately bought by Google and has been on the forefront of deep learning research ever since.

It is very likely that you've heard all the buzz that has been going lately about the chatbots, and how they're going to revolutionize everything in the coming years, but if you haven't, let me guide you through the revolution. Well, fear no more, dear reader, this is (part one of) all you need to know about chatbots. In general terms, a bot is a piece of software that automates a task, but talking specifically about chatbots, we come to the concept of automating an interaction through a conversational UI. But don't mind my fancy wording. Chatbots are a way in which you can automate a written conversation, simulating an interaction between two real human beings.

In a previous post, we covered various methods of object detection using deep learning. In this blog, I will cover Single Shot Multibox Detector in more details. SSD is one of the most popular object detection algorithms due to its ease of implementation and good accuracy vs computation required ratio. Work proposed by Christian Szegedy is presented in a more comprehensible manner in the SSD paper https://arxiv.org/abs/1512.02325. This method, although being more intuitive than its counterparts like faster-rcnn, fast-rcnn(etc), is a very powerful algorithm. Being simple in design, its implementation is more direct from GPU and deep learning framework point of view and so it carries out heavy weight lifting of detection at lightning speed. Also, the key points of this algorithm can help in getting a better understanding of other state-of-the-art methods.

New advances in artificial intelligence (AI) and machine learning (ML) research made by tech giants and academia have quickly made their way into businesses and business models, while even more companies are introducing established AI solutions like chatbots and virtual assistants. Following all that is happening in the dynamic world of AI is time-consuming for entrepreneurs who are busy running their own companies, so I've compiled a list of the most interesting AI trends entrepreneurs should keep an eye on in the coming year. The trend toward humanization of big data and data analytics will continue in 2018 with new advancements in natural language generation (NLG) and natural language processing (NLP). Using rule-based systems like Wordsmith by Automated Insights, media outlets and companies can already turn structured data into intelligent narratives based on natural language. Making relationships in data understandable to people beyond data science teams will further democratize AI and big data, leading to the era of automatic generation of insights.

Professionals from across the tech industry foresee widespread AI disruption. Many are positive about the impending developments, but hackers will also have access to the same capabilities. While many lament the use of the term'artificial intelligence' for machine learning or automation technology, it has become the catch-all name for an impending tide of disruption across industries. Organisations including IBM and Microsoft have been spearheading progress in the space, with the two giants vying for dominance in terms of deep learning capabilities. Organisations like Facebook are also pioneering AI research; this year Facebook researchers discovered its AIs communicating with one another and learning, resulting in a fascinating story that even had mainstream news consumers captivated.

Artificial intelligence is revolutionizing at a speed of vertigo and has brought about the interest of big companies thanks to its promising advances, many already a reality. It is, in fact, the so-called theme of Deep Learning, a technology of learning and classification based on networks of numerical artificial neurons. But it was not always like this. The success has come to Deep Learning after a long journey in the desert. While the current enthusiasm atmosphere may make us believe in impossible, a problem that experts warn like Yann LeCun, the truth is that this technology is still a big question in many aspects. Likewise, Deep Learning raises questions about the possible danger of its evolution.

John Schwarz, co-founder and CEO of Visier, is responsible for the company's overall strategy, culture and organization development. In an earlier interview, Schwarz explained the risks of corporate toxicity in the digital age, what companies can do to address this and the services that his company can offer. In a follow-up interview John Schwarz focuses on the most interesting technological developments for business. Digital Journal: In the previous interview you explained how Visier Workforce Intelligence is a cloud-based business analytics solution that lets large enterprises analyse and plan their workforce. What other services does Viser offer?

The Industrial (IIoT) is characterized by highly automated and universally networked production flows. In this area, machine vision is becoming increasingly more important as a key technology. As the "eye of production," machine vision processes digital image information generated by cameras and is, therefore, able to identify a wide range of objects that then can be reliably allocated and handled throughout the entire value chain. To make the identification process even more precise and adapt it to the requirements of flexible and networked IIoT processes, AI-based methods such as deep learning and CNNs, are becoming more prevalent in machine vision. The challenge for embedded systems is that, compared to stationary desktop systems, embedded systems are more limited in terms of their processors, memory, and storage capacity, and therefore have less computing power. One technological proof point that that brings deep learning to the nVidia Pascal architecture comes from MVTec Software.

A lot of computational power is needed to solve deep learning problems because of the iterative nature of deep learning algorithms, their complexity as the number of layers increase, and the large volumes of data needed to train the networks. The dynamic nature of deep learning methods – their ability to continuously improve and adapt to changes in the underlying information pattern – presents a great opportunity to introduce more dynamic behavior into analytics. Greater personalization of customer analytics is one possibility. Another great opportunity is to improve accuracy and performance in applications where neural networks have been used for a long time. Through better algorithms and more computing power, we can add greater depth.

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