The global population is expected to reach 9 billion people by 2050, which means double agricultural production in order to meet food demands. Farm enterprises require new and innovative technologies to face and overcome these challenges. Artificial intelligence robotics is one of these technologies that promises to provide a solution. An increasing number of farmbots are being developed that are capable of complex tasks that have not been possible with the large-scale agricultural machinery in the past. Here's a list of real use cases of robots that will help agriculture changing.

SAN FRANCISCO -- Google is creating tools for software developers to build chat bots that run inside messaging apps such as Facebook Messenger and Google's own messaging products, according to a published report. The technology giant plans to discuss some of the details at its annual developer conference, which starts Wednesday, The Information reported. "Essentially, Google wants to get into the bot-making business more broadly than people realized, and in a way that's similar to Microsoft's strategy, and relying on its strengths in artificial intelligence to convince developers to use its tools," the report reads. Google, like Microsoft, missed out on the new wave of messaging apps which are some of the world's most popular apps. The Wall Street Journal reported in December that Google planned to integrate chat bots, interactive software powered by artificial intelligence that answer questions inside a messaging app, so that users can send queries as text messages.

Lots of people yell things at their televisions. Nowadays, however, it's a lot more likely that your television is going to talk right back. Amazon on Tuesday announced that it is adding new features to its voice-activated app, Alexa, as it works with its Fire TV set-top box, empowering the assistant to start videos and search for movies or television shows. You can even look up local restaurants if you need dinner after your binge-watching session ate up your lunch. The company said that those features and others, such as being able to have Alexa read you your Kindle e-books through your Fire TV, would launch in the "coming weeks."

Following examples set by the likes of Google and others, Amazon has made its Deep Scalable Sparse Tensor Network Engine (DSSTNE) generally available to researchers, developers and everyone else. The engine, which is used to provide product recommendations to Amazon shoppers -- usually under the "You may also be interested in" -- is now available on Github. The package includes examples, instructions for setup, FAQs, User guide and holds a business-friendly Apache 2.0 license. We are releasing DSSTNE as open source software so that the promise of deep learning can extend beyond speech and language understanding and object recognition to other areas such as search and recommendations. We hope that researchers around the world can collaborate to improve it.

This post provides an in-depth introduction to using Keras for deep language modeling. Includes sections on word embedding, characterizing recurrent and convolutional neural networks, attentional RNNs, and similarity metrics for sentence vectors. Each section includes examples on how to implement it using Keras. This post explains the code in this Github repository. Question answering has received more focus as large search engines have basically mastered general information retrieval and are starting to cover more edge cases. Question answering happens to be one of those edge cases, because it could involve a lot of syntatic nuance that doesn't get captured by standard information retrieval models, like LDA or LSI. Hypothetically, deep learning models would be better suited to this type of task because of their ability to capture higher-order syntax. Two papers, "Applying deep learning to answer selection: a study and an open task" (Feng et.

Management consulting company Accenture has teamed up with IT automater IPsoft to launch an artificially intelligent agent called Amelia, who has been designed to encourage companies to embrace machine learning. Similar to Apple's Siri, Amelia will tackle client queries in "conversational" language, answering questions and managing processes. For example, she is able to help customers open bank accounts, or check out insurance policies. She will also be on hand to help employees within businesses, providing internal services like HR and guiding staff on company policies. As well as boasting natural language capabilities, Amelia has an element of machine learning - meaning she can be taught how to carry out various functions, like answering calls, via a simple uploading process.

If you were learning to code, you screwed up. Less than a year ago, Bloomberg was answering the question of "What is code?" and now we don't even need it anymore. Tanz is correct that machine learning is a new (for most people) and exciting way for humans to interact with computers. Instead of requiring coding knowledge, programs, functions, and algorithms can be improved more or less by training them. Large companies are already doing this, and the thing that has gone unmentioned in Tanz's piece is that, well, only large companies can do this.

The idea of artificial intelligence (AI) has been around for thousands of years, dating back to some of the earliest Greek myths. Those early stories show our infatuation with the concept that we could imbue machines with that most human of qualities: independent thought. Like many of humanity's greatest technical achievements, the quest to create AI is fundamentally about making our lives easier. We are now entering an era where the reality of AI is beginning to catch up with the myths and science fiction stories of our youth. Advances in the area of machine learning (ML) and natural language processing (NLP) have resulted in devices and applications that we interact with daily.

Smarter and more adaptive machines are rapidly becoming as much a part of our lives as the internet, and more of our decisions are being handed over to intelligent algorithms that learn from ever-increasing volumes and varieties of data. As these "robots" become a bigger part of our lives, we don't have any framework for evaluating which decisions we should be comfortable delegating to algorithms and which ones humans should retain. That's surprising, given the high stakes involved. I propose a risk-oriented framework for deciding when and how to allocate decision problems between humans and machine-based decision makers. I've developed this framework based on the experiences that my collaborators and I have had implementing prediction systems over the last 25 years in domains like finance, healthcare, education, and sports.

As a taxi driver, you need to pick up and drop off passengers as fast as possible.