We read a lot of news about chatbots reshaping entire industry sectors by utilizing artificial intelligence (AI), machine learning and natural language processing. Some chatbots are good in assisting consumers in buying tickets or finding good food nearby. Others can keep a simple conversation alive or replace traditional FAQ pages. What most media pundits miss, however, is that such a capability is nowhere near general AI potential, which casts doubts over the very future of chatbots. For start, chatbots are of two basic types: conversational and goal-oriented.
Chida Chidambaram Vishal Deshpande BDT311 Deep Learning Going Beyond Machine Learning October 2015 2. What to Expect from the Session Data analytics options on AWS Machine learning (ML) – high level Amazon ML from AWS ML sample use case Deep learning (DL) – high level DL sample use cases AWS GPU/HPCC server family Q&A 3. Data Analytics Options on AWS Amazon EMR AnalyzeStoreIngest Amazon Kinesis DynamoDB Amazon Redshift RDSS3 Amazon Kinesis Consumer Machine Learning Amazon Kinesis Producer Traditional Server Mobile Clients EC2 Machines 5. Machine Learning How can a machine identify Bruce Willis vs Jason Statham? Bruce Willis??? 6. Machine Learning Machine Learning Artificial Intelligence Optimization & Control Neuroscience and Neural Networks Statistical Modeling Information Theory 7. Machine Learning Bear Eagle People Sunset 8. Machine Learning • Using machines to discover trends and patterns and compute mathematical predictive models based on factual past data • ML models provide insights into likely outcomes based on the past – machine learning helps uncover the probability of an outcome in the future rather than merely state what has already happened in the past • Past data and statistical modeling is used to make predictions based on probability Where traditional business analytics aims at answering questions about past events, machine learning aims at answering questions about the possibilities of future events 9. Machine Learning Supervised learning Human intervention and validation required Photo classification and tagging Unsupervised learning No human intervention required Auto-classification of documents based on context 10. Machine Learning – Process How can a machine identify Bruce Willis vs Jason Statham? Image analysis – Input feature set for image 1 - bald, black suit Bruce Willis??? 14. Machine Learning – Process • Start with data for which the answer is already known • Identify the target – what you want to predict from the data • Pick the variables/features that can be used to identify the patterns to predict the target • Train the ML model with the dataset for which you already know the target answer • Use the trained model to predict the target on the data for which the answer is not known • Evaluate the model for accuracy • Improve the model accuracy as needed 15. Machine Learning – When to Use It You need ML if • Simple classification rules are inadequate • Scalability is an issue with large number of datasets You do not need ML if • You can predict the answers by using simple rules and computations • You can program predetermined steps without needing any data driven learning 16.
The iPhone X will change everything when it arrives next month. It'll herald in a brave new notch-filled world with no home buttons and Face ID, a new face-recognition technology that unlocks the phone when you look at it. Mere weeks away from launch and a month after Sen. Al Franken (D-MN) penned a letter to Apple CEO Tim Cook voicing privacy concerns over Face ID, Apple has finally responded to his questions in what's clearly a move to pacify any lingering fears over its new biometric technology. SEE ALSO: Why you'll be forced to buy a case for your iPhone X Apple provided Mashable with a copy of the letter Cynthia Hogan, the company's VP for Public Policy, sent to Sen. Franken. On behalf of Apple, Hogan reiterates how Face ID works using the iPhone X's TrueDepth camera and sensors to scan and analyze a user's face based on depth maps and 2D images it creates.
Google revealed the Pixel 2's and Pixel 2 XL's HDR and machine learning capabilities for computational computing will be the result of work by the Pixel Visual Core, the company's first-ever own-design co-processor. Together, they can perform more than 3 trillion calculations per second -- allowing for up to five times the processing speed for HDR at 10 percent of the power usage. It's able to glide between domain-specific languages (Halide for images and Google's TensorFlow for machine learning) to make things easier for third-party developers. The Pixel Visual Core will be enabled on Pixel 2 (and, presumably, Pixel 2 XL) devices with the Android 8.1 Oreo update (Maintenance Release 1) and third-party apps will be able to crack at code to harness the power of this new hardware. Rumors of Google developing its own mobile applications processor have been around for years.
There's growing excitement – admittedly, at times, borderline hype – about what artificial intelligence can, and will, do for businesses. While speculation abounds among pundits, journalists, and'thought leaders' surrounding the impact that AI will have on jobs (CBInsights predicts 10 million jobs are at risk in the next 5-10 years), there's relatively little analysis of the tangible effect AI will have on marketer's day-to-day work, and the opportunity to'upskill' us all. Writing exclusively for ExchangeWire, Gareth Davies (pictured below), founder and CEO, Adbrain, explains why and how artificial intelligence can realise tangible benefits for marketers. Today's marketers will benefit by navigating an increasingly AI-centric (and AI-literate) world where bots, intelligent software and machine learning play an increased role in the marketing function. To help you cut through the noise, here are some tangible examples of where AI is likely to become a relevant part of the modern marketers' workflow, as well as ideas on how to better understand and qualify the impact that AI can have on your business.
Figuring out the best way to market your products and/or services to your customers can be a tricky business. According to marketing trends for 2017, it doesn't look like it's getting any easier for businesses to effectively market their content either. Given all the additional resources a business has access to, one would think they would have a significant advantage when it comes to marketing, however no matter how much research a company does beforehand, the results of marketing campaigns are always unpredictable. As companies look to increase their sales growth and their customer base, it's only natural that they are looking for new and creative ways to increase the effectiveness of their marketing campaigns and bring back the maximum return on each dollar spent. In order to maximize the effectiveness of their marketing campaigns, businesses and individuals need to take a step back and consider new approaches.
The AI systems will be able to learn over time through analysing how human lawyers complete the tasks and will ultimately be able to process the cases much faster – freeing up time for lawyers to focus on the more complex and cognitive parts of the case. One of the first steps for a business looking to integrate AI into their workforce is to identify processes that would benefit from integrating with the technology – there's no use bringing AI systems into the office if they're not going to help anyone. Business leaders then need to explore all of the options available for applying a pre-built learning system to handle those identified tasks, and reap the improvements in scalability and throughput that AI enables. Now is the time for businesses to start investigating and experimenting with AI, to reap the scalability and efficiency benefits and stay ahead of the competition.
AI will play an increasingly important role in the top three business objectives often cited by CEOs -- greater customer intimacy, increasing competitive advantage and improving efficiency. But commercial uses of AI are in specialised industry-specific applications such as actuarial forecasts and medical diagnosis -- making CIOs understandably cautious about promoting AI's potential business value. While most organisations may not pursue these leading-edge uses of AI, it will play an increasingly important role in the top three business objectives often cited by CEOs -- greater customer intimacy, increasing competitive advantage and improving efficiency. These skills include technical knowledge in specific AI technologies, data science, quality data maintenance, problem domain expertise, as well as skills to monitor, maintain and govern the environment.
We can use the split() function to split the loaded document into tokens separated by white space. We can use the data cleaning and chosen vocabulary to prepare each movie review and save the prepared versions of the reviews ready for modeling. One approach could be to save all the positive reviews in one file and all the negative reviews in another file, with the filtered tokens separated by white space for each review on separate lines. We can then call process_docs() for both the directories of positive and negative reviews, then call save_list() from the previous section to save each list of processed reviews to a file.
NVIDIA GPUs have been on the forefront of accelerated neural network processing and are the de facto standard for accelerated neural network research and development (R&D) plus deep learning training. At the NVIDIA GPU Technology Conference (GTC) in Beijing China earlier this week, the company maneuvered to also become the de facto standard for accelerated neural network inference deployment. At GTC Beijing, NVIDA lined up the major Chinese cloud companies for AI computing: Alibaba Cloud, Baidu Cloud, and Tencent Cloud. At GTC-Beijing, it announced inference designs with Alibaba Cloud, Tencent, Baidu Cloud, JD.com, and iFlytek.