Move over, managers, there's a new boss in the office: artificial intelligence. The same technology that enables a navigation app to find the most efficient route to your destination or lets an online store recommend products based on past purchases is on the verge of transforming the office--promising to remake how we look for job candidates, get the most out of workers and keep our best workers on the job. These applications aim to analyze a vast amount of data and search for patterns--broadening managers' options and helping them systematize processes that are often driven simply by instinct. And just like shopping sites, the AIs are designed to learn from experience to get an ever-better idea of what managers want. A company can provide a job description, and AI will collect and crunch data from a variety of sources to find people with the right talents, with experience to match--candidates who might never have thought of applying to the company, and whom the company might never have thought of seeking out.

Anyone who's used the internet in the last decade should be immediately familiar with the reCAPTCHA system (Completely Automated Public Turing test to tell Computers and Humans Apart), the little box on websites that has users type in distorted words or click on a checkbox to prove they're human. Well, this could soon all be a thing of the past, as Google has revealed it's developed a way to make the reCAPTCHA system completely invisible, with no need for human input. As Google explains, the Invisible CAPTCHA system uses a "combination of machine learning and advanced risk analysis that adapt to new and emerging threats." In plainspeak that means that the system automatically begins detecting user habits when they arrive on a webpage, including things like mouse movements and IP address. Assuming everything checks out, the transcription puzzle or checkbox won't be necessary, and the page's content will load like normal.

Whenever i think about the impact that deep learning will have in Africa in the next 5–10 years from now, My curiosity and passion to understand and implements deep learning increases. Improving Agriculture yields in Africa with deep learning becomes my first focus. As a result of these reasons, it is now possible for anyone to apply deep learning techniques to real problems in a way that is both affordable and fast.

A Facebook tool that helps students be more productive and keep track of assignments developed by a Victoria teen has gone viral in an unexpected place. Alec Jones, 14, says his chatbot, Christopher Bot, that helps students stay on top of their homework has garnered more than 3,000 subscribers, with many of them based in Thailand. Chatbots are automated software programs, which Alec compares to iPhone's Siri, that respond to users in a conversational manner. The bot that he developed works through Facebook's Messenger platform. For students who forget to write down their assignments or deadlines when they are handed out or struggle to keep track of it all, Christopher Bot is designed to do the remembering for them.

Note to reader: Diving into this material here can be a bit overwhelming. Deep Learning Architecture can be described as a new method or style of building machine learning systems. Deep Learning is more than likely to lead to more advanced forms of artificial intelligence. The evidence for this is in the sheer number of breakthroughs that had occurred since the beginning of this decade. There is a new found optimism in the air and we are now again in a new AI spring.

This post is part of the author's Learning Machine Learning series. It's based on Chapter 1 and 2 of Python Machine Learning. Machine learning can be intimidating for a newcomer. The concept of a machine learning things alone is quite abstract. How does that work in practice? In order to demystify some of the magic behind machine learning algorithms, I decided to implement a simple machine learning algorithm from scratch.

What are some of the applications and use cases? Machine Learning (ML) and the Internet of Things (IoT) are huge buzzwords right now, and they're both near the peak of the hype cycle. The above quote came somewhat jokingly from an investor, but it has some truth to it too. Given all the hype and buzz around machine learning and IoT, it can be difficult to cut through the noise and understand where the actual value lies. In this week's #askIoT post, I'll explain how machine learning can be valuable for IoT, when it's appropriate to use, and some applications and use cases currently out in the world today.

With the progressing year, the technology diversified ways in which we could communicate and retrieve the information from the pocket fitting devices. Technologies such as IoT, automation, and cognitive computing moved beyond the conceptual stages in 2016. As the year takes up, companies throughout the world are developing their business strategies. In order to move forward in the competition, companies are turning towards major investments in technology. The world's biggest consumer technology convention, CES is one of the best places to find a handful of key technologies. CES 2017 finished another spectacular year with pioneering technology trends including smart homes to self-driving cars. This year is assumed to bring transformative technology trends for us to explore and invest in. AI, also known as Artificial Intelligence has been studied for decades and now the vision of transforming insentient objects into intelligence is gradually becoming a reality. AI based Innovations are now pondering into the market and becoming part of our daily lives with quick adaptability. Artificial intelligence assists humans and handles the tasks flawlessly, without interrupting your comfort. Whether to set an alarm, or remind you of something important, or to play your favorite music or to read out general news for you or to find your phone, AI can make the task more convenient and smart. Sit back and relax while you command your device to do things for you.

Reflecting the rapidly increasing interest and investment in cloud computing, 10,000 developers, engineers, IT executives, and Google employees and partners gathered at Next '17, Google's annual cloud event for enterprise customers. Google showcased customer testimonials from Disney, Verizon, HSBC, Colgate-Palmolive, and Ebay; support from a number of new partners, including SAP; and a series of AI and cloud infrastructure-related announcements. Analysts were not impressed (see here and here, for example). Google, the argument goes, is a consumer company and does not understand or have ready access to enterprise customers. Amazon's first mover advantage has produced an insurmountable market lead (last year, it reported cloud revenues of $12.22 billion, compared to Microsoft's $2.42 billion and Google's $900 million, Deutsche Bank estimates).

Artificial intelligence (AI) is going mainstream, which has long-lasting implications for how enterprises build and improve their products and services. As announced at re:Invent 2016, AWS is simplifying artificial intelligence (AI) adoption through three low-cost, cloud-based services built for AI-specific uses cases. Instead of creating proprietary algorithms, data models or machine learning techniques, all levels of developers from Global 2000 enterprises through start-ups can leverage the Amazon Lex, Amazon Rekognition and Amazon Polly APIs to innovate quickly and build new Internet of Things (IoT) product and service offerings. Accenture, is delivering these innovative offerings by supporting clients with vertical industry applications powered by Amazon AI. Combining AI with IoT is essential because it enables businesses to collect data in the physical world–from wearables, appliances, automobiles, mobile phones, sensors and other devices--and add intelligence to deliver a better response or outcome.