Parameter efficient finetuning methods like low-rank adaptation (LoRA) aim to reduce the computational costs of finetuning pretrained Language Models (LMs). Enabled by these low-rank settings, we propose an even more efficient optimization strategy: Fast Forward, a simple and effective approach to accelerate large segments of training. In a Fast Forward stage, we repeat the most recent optimizer step until the loss stops improving on a tiny validation set. By alternating between regular optimization steps and Fast Forward stages, Fast Forward provides up to an 87\% reduction in FLOPs and up to an 81\% reduction in train time over standard SGD with Adam. We validate Fast Forward by finetuning various models on different tasks and demonstrate that it speeds up training without compromising model performance. Additionally, we analyze when and how to apply Fast Forward.

This article proposes an adaptation from the model of Bianco for fast-forwarding agents in crowd simulation, which enables us to accurately fast forward agents in time. Besides being able to jump from one position to another, agents are able to stay inside their track, it means, the new position is calculated taking into account the original global path the agent would follow, if not being fast-forwarded. Obstacles and other agents around are also taken into account when calculating the new position. In addition, we included a personality aspect on agents, which affect their behaviors and, also, be taken into account when jumping to a future time and space. We conducted some experiments to validate our model, which shows that it was able to indeed fast forward agents from a position to another, in a coherent time, sticking to a given global path while avoiding collisions. Finally, we present a use case, showing that our method can fit inside a "Fog of War" system.

Let us go back a few years. Do you remember the movie Robot starring Rajinikanth? Chitti, played by Rajinikanth, had the ability to follow and exhibit human emotions. What was once in the realm of films, fiction and fantasy is becoming a reality over time. And time is really moving fast.

Let us go back a few years. Do you remember the movie Robot starring Rajinikanth? Chitti, played by Rajinikanth, had the ability to follow and exhibit human emotions. What was once in the realm of films, fiction and fantasy is becoming a reality over time. And time is really moving fast.

The idea of access to artificial intelligence (often referred to as "AI" for short) for all is still at a nascent stage. Nevertheless, its potential is limitless – from refugee assistance, to helping students at risk of not completing school, to shortening the response time for teens who are in a personal crisis. During the recent Dreamforce conference in San Francisco, several nonprofit leaders discussed how the use of artificial intelligence and other technologies, such as machine learning, can dramatically improve lives – including those of many who live in the most underserved communities. Some of those citizens now benefiting from artificial intelligence include college and university students in Texas. College Forward, based in Austin, offers coaching and mentoring programs to help at-risk students continue their success in higher education so that they can eventually embark on a successful career.

Fast forward to 2017 I have spent 100's of hours working on Deep learning projects and the technology has become more and more accessible due to several advancements in software(ease of usage -- Keras, PyTorch), hardware(GPU becoming commercially viable for someone like me sitting in India -Not still cheap), availability of data, good books and MOOCS. After completing the 3 most popular MOOCS in deep learning from Fast.ai, deeplearning.ai/Coursera In this post I talk about 5 aspects of each course which will help you decide. I came across this course when reading an article in kddnudgets . For the first time I heard about Jeremy Howard, searched about him in Wikipedia and was impressed .

In recent years, we've become addicted to on-demand media, entertainment and information services. Rolling news, something relatively new 20 years ago, is now the norm. Binge-watching online TV shows has become part of our lives. As demand for this content grows, technology must find new ways to deliver these services and provide highly-customised and engaging experiences. Step forward Artificial Intelligence (AI) which is set to have a massive impact in the digital era.

Let's be honest: Reaching for the remote while watching TV isn't a major inconvenience. If you can control your TV by voice instead, why wouldn't you? To that end, Google and Amazon now offer hands-free TV controls through their respective connected speakers. Amazon added Fire TV voice controls to the Echo speaker and other Alexa devices a couple weeks ago, while Google has offered similar voice controls for Chromecast through its own Google Home speaker since last December. Both combos allow you to launch videos, pause, play, rewind, and fast forward without ever touching a remote control.

Predictions for the potential of artificial intelligence wax poetic -- solutions from climate change to curing disease -- but the everyday applications make it seem far more mundane, like a glorified clock radio. Thankfully, the future may be closer than we think. And the miraculous feats are not happening in Silicon Valley X-Labs -- in a plot twist, nonprofits are leading the charge in creating human-centered applications of the hottest AI technologies. From the simplest automated communications to contextual learnings based on analysis of deep data, these technologies have the potential to rapidly scale and improve the lives of our most underserved communities. Take chatbots for example, a new spin on mobile messaging that has historically been human-powered.

We'll be discussing Watson, of course, but also artificial intelligence, machine learning, and--most importantly--how businesses use these tools to better understand their customers, partners, and how they operate their businesses. Read or watch our full discussion below. Dan Costa: A lot of people have seen the IBM Watson commercials on TV, and they know that Bob Dylan had something to do with this in some vague way, but how would you define Watson as a product? Mark Simpson: Watson's a cognitive computing product, which can learn like humans. It learns, it understands, it reasons in the same way that humans do and can be taught over time. So that computing can be applied into many different areas ... Essentially it is a trusted advisor that we can give to humans that can take in masses of data and help them in the decisions that they make, augmenting their intelligence. I think that's the phrase that has stuck with a variety of people.