Using neural networks in practical settings would benefit from the ability of the networks to learn new tasks throughout their lifetimes without forgetting the previous tasks. This ability is limited in the current deep neural networks by a problem called catastrophic forgetting, where training on new tasks tends to severely degrade performance on previous tasks. One way to lessen the impact of the forgetting problem is to constrain parameters that are important to previous tasks to stay close to the optimal parameters. Recently, multiple competitive approaches for computing the importance of the parameters with respect to the previous tasks have been presented. In this paper, we propose a learning to optimize algorithm for mitigating catastrophic forgetting. Instead of trying to formulate a new constraint function ourselves, we propose to train another neural network to predict parameter update steps that respect the importance of parameters to the previous tasks. In the proposed meta-training scheme, the update predictor is trained to minimize loss on a combination of current and past tasks. We show experimentally that the proposed approach works in the continual learning setting.

Artificial intelligence (AI) stepped out of the research labs and into the limelight at the 17th Cebit exhibition and conference held in Sydney, Australia. Every conference this year contains a dead human genius reincarnated as software system or a robot. Yes, there is a lot of hype, but there is real worth in AI and Machine Learning. Read our counseling on how to avoid adopting "black box" approach. You forgot to provide an Email Address.

Monday, the 29th, marked the beginning of the EU Green Week, an event organized by the European Commission's Directorate-General for Environment to discuss environmental policies. This year, the focus is "Green jobs for a greener future." The organizers stressed how traditional specializations will be characterized by additional sets of new skills. Being able to deal with technology is certainly one of them, and many jobs in the environmental sciences are already adopting these innovative tools. People working in this sector are no longer restricted to field work and laboratory analyses.

Overall, automation may be more likely to change jobs than to destroy them. Get ready for a new career as an "empathy trainer", "explainability strategist," or perhaps as an "artificial intelligence safety engineer". As new technologies cut a swathe through traditional white collar jobs, these are some of the new roles that could rise up in their place, according to a new book by Accenture chief technology officer Paul Daugherty and fellow researcher Jim Wilson. It is a topsy-turvy world perhaps, where economic success is defined as ensuring there is enough work to keep everyone gainfully employed. But that hasn't stopped people through the ages fretting that new technology may leave us all paupers.

Getty Close-up adult hand typing on laptop A centre for artificial intelligence and public policy is looking to address the unique issues New Zealand will face, and is currently facing, in the era of AI. The centre has been launched in Otago and will explore policy options for managing the introduction of technologies, to maximise their benefits and minimise potential harms. Co-director of the centre, Professor James Maclaurin, said New Zealand's size sets it apart from other countries and it is important to have people acting in an advisory role. "Europe has just passed its general data protection regulations but it is a very big player so if they pass laws Facebook and Google really have to listen to them. "New Zealand is a different environment."

LexNLP is an open source Python package focused on natural language processing and machine learning for legal and regulatory text. The package includes functionality to (i) segment documents, (ii) identify key text such as titles and section headings, (iii) extract over eighteen types of structured information like distances and dates, (iv) extract named entities such as companies and geopolitical entities, (v) transform text into features for model training, and (vi) build unsupervised and supervised models such as word embedding or tagging models. LexNLP includes pre-trained models based on thousands of unit tests drawn from real documents available from the SEC EDGAR database as well as various judicial and regulatory proceedings. Keywords: natural language processing, legal, regulatory, machine learning, segmentation, extraction, open source, Python 1. Introduction Over the last two decades, many high-quality, open source packages for natural language processing and machine learning have been released. Researchers and developers can quickly write applications in languages such as Java, Python, and R that stand on the shoulders of comprehensive, well-tested libraries like Stanford NLP ([1]), OpenNLP ([2]), NLTK ([3]), spaCy ([4]), scikit-learn ([5], [6]), Weka ([7]), and gensim ([8]).

A centre for artificial intelligence and public policy is looking to address the unique issues New Zealand will face, and is currently facing, in the era of AI. The centre has been launched in Otago and will explore policy options for managing the introduction of technologies, to maximise their benefits and minimise potential harms. Co-director of the centre, Professor James Maclaurin, said New Zealand's size sets it apart from other countries and it is important to have people acting in an advisory role. "Europe has just passed its general data protection regulations but it is a very big player so if they pass laws Facebook and Google really have to listen to them. "New Zealand is a different environment."

Epileptic seizures strike with little warning and nearly one third of people living with epilepsy are resistant to treatment that controls these attacks. More than 65 million people worldwide are living with epilepsy. For more information see the IDTechEx reports on digital health 2018 and wearable technology. Now researchers at the University of Sydney have used advanced artificial intelligence and machine learning to develop a generalised method to predict when seizures will strike that will not require surgical implants. Dr Omid Kavehei from the Faculty of Engineering and IT and the University of Sydney Nano Institute said: "We are on track to develop an affordable, portable and non-surgical device that will give reliable prediction of seizures for people living with treatment-resistant epilepsy."

Google has chosen India as a major battleground to take on rivals Amazon and Microsoft in its bid to dominate cloud computing services, said Rick Harshman, MD-Asia Pacific for Google Cloud, in an interview. He said the company had made big strides in the country in terms of enterprises adopting its technologies such as cloud services, security, artificial intelligence and machine learning. How are Indian enterprises adopting your technologies, especially cloud and artificial intelligence? How large is the opportunity? Globally... only about 5%-10% of all workloads in IT run on the cloud. I think the estimates are quite conservative.