If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Digital transformation is the key imperative in the corporate suite of most forward-thinking enterprises. IDC coined the term Digital Darwinism to reflect the impact of digital transformation on businesses of all sizes and across industries. According to IDC, organizations are moving away from business as usual and embracing digital transformation to become more competitive. Key components of enacting digital transformation are the applied sciences of artificial intelligence, machine learning, deep learning, and prescriptive analytics, the creation of computational systems that allow autonomous decision making. Through prescriptive analytics, organizations will redefine how business decisions are made.
Categorically, artificial intelligence (AI) can appear be an odd juxtaposition of order and disorder -- we direct the AI with algorithms, yet the system produces new insights seemingly magically. Most of the well-known applications of machine learning and computational AI involve supervised learning. The modeler amasses a vast set of existing data (e.g., financial transactions, internet photographs, or the texts of tweets) and a base-level "ground truth" outcome that is already known, perhaps in retrospect or by expensive human investigation. Equipped with any number of computational algorithms, the scientist becomes the "supervisor" whose code trains the model to reproduce, in the lab, the known outcomes with a low probability of error. The models are then deployed to live a happy life scoring credit risk and fraud likelihood, finding pictures of Chihuahuas and muffins, or flagging insulting tweets.
We've compiled a list of the hottest events and conferences from the world of Data Science, Machine Learning and Artificial Intelligence happening in 2018. Below are all the links you need to get yourself to these great events! Please get in touch if there are any great events or conferences you think should be added!
In recent times, computation has become both pervasive and less constrained by Moore's Law. This is due in large part to the emergence of cloud computing and the rise of massive parallelism. The former has benefited from network improvements and ever increasing connectedness, the latter from the appropriation of hardware like Graphics Processing Units (GPUs) for general purpose computing. This computational leap, coupled with the process of disintermediation  taking place around the globe will continue to support revolutions like artificial intelligence (AI), as many have remarked. AI has a long and interesting history.
MeetAI London and NeurotechX want to join efforts and bring together a selected panel of experts in diverse aspects of machine learning and neuroscience. An open discussion centred around how this two fields work together, the current achievements, and the future goals and limitations. Neuroscience and artificial intelligence are heavily related and both are living a golden age. Machine learning has been inspired by the nervous systems since its first steps. Terms such as neural networks or reinforcement learning have been borrowed from natural sciences and translated into silicon.
Deep neural networks and Deep Learning are powerful and popular algorithms. And a lot of their success lays in the careful design of the neural network architecture. I wanted to revisit the history of neural network design in the last few years and in the context of Deep Learning. For a more in-depth analysis and comparison of all the networks reported here, please see our recent article. Reporting top-1 one-crop accuracy versus amount of operations required for a single forward pass in multiple popular neural network architectures.
Candidates for this position must have a PhD or equivalent. Applicants should be actively engaged in research and have teaching experience in data science, applied artificial intelligence (AI), machine learning, or computational statistics. Rank and salary are commensurate with qualifications and experience. We encourage candidates who are interested in opportunities for collaborative and interdisciplinary research. We have active and well-established research groups in areas such as biomedical informatics, recommender systems, data visualization, and web data mining.
I suppose you could call it nostalgia, but that word has a connotation of sadness and loss that does not properly fit here. We don't understand much about how the brain truly functions, let alone the intricacies of consciousness. But perhaps we can think of these ebbing and waning thought cycles as waves, even curves, on a two dimensional plane that I would like to call a Mind Map. In discussing this abstraction with my friend @openmylab in Sydney, we have been drawing 10 year Mind Maps of our own brains in order to identify areas of intellectual passion as well as intersections between seemingly disparate curves that have become inflection points in our lives. In both cases we started with a few rules to make the map visibly appealing and uncluttered.
Machine learning is one of the hottest areas of development, but most of the attention so far has focused on the cloud, algorithms and GPUs. For the semiconductor industry, the real opportunity is in optimizing and packaging solutions into usable forms, such as within the automotive industry or for battery-operated consumer or IoT products. Inefficiencies often arise because of what is readily available, and that is most certainly the case with machine learning. For example, GPUs have been shown to be the highest-performance solution for training. Because these devices are based on floating point, then machine learning algorithms are developed that rely on floating point.
It is the year 2019. You are sitting with your laptop at the kitchen table of your home with your best friend. You've been sitting there a lot lately, tweaking the software for a startup you are working on together. A minute ago you were both cheering but something has changed the mood. You are looking down on the keyboard of your laptop, slowly moving your fingers to grace the keys you were pressing frantically a few minutes ago.