Natural Language

"Alexa, help me find a job at McDonald's." That's how interested job seekers can start an application with the global fast-food company, McDonald's recently announced. Claiming it to be the world's first voice-initiated job application process, the company has launched McDonald's Apply Thru, which works on Amazon Alexa and Google Assistant. The app is currently available in the United States, Australia, Canada, France, Germany, Ireland, Italy, Spain and the United Kingdom and is expected to roll out to other countries in the coming months. Once Alexa or Google Assistant responds, users are asked to provide basic information, such as their name, contact information, job area of interest and location. Potential applicants then receive a text message with a link to the McDonald's careers site to continue their application process.

This year, we saw a dazzling application of machine learning. The OpenAI GPT-2 exhibited impressive ability of writing coherent and passionate essays that exceed what we anticipated current language models are able to produce. The GPT-2 wasn't a particularly novel architecture – it's architecture is very similar to the decoder-only transformer. The GPT2 was, however, a very large, transformer-based language model trained on a massive dataset. In this post, we'll look at the architecture that enabled the model to produce its results. We will go into the depths of its self-attention layer. My goal here is to also supplement my earlier post, The Illustrated Transformer, with more visuals explaining the inner-workings of transformers, and how they've evolved since the original paper. My hope is that this visual language will hopefully make it easier to explain later Transformer-based models as their inner-workings continue to evolve.

AI has been talked about since the very early days of computing and has attained mainstream use in recent years with the likes of Amazon's Alexa and Apple's Siri. "Just as in the last 40 years, computation has enabled us to change the way we do business and create new products, AI will help us to make better decisions," Carlos Kuchovsky, chief of technology and R&D at BBVA, tells Finextra. "We are now looking at the ways in which it can help us change the way we operate and bring value." The Bank of England has recently reported that machine learning tools are in use at two thirds of UK financial firms, with the average company using it two business areas, which is expected to double in the next three years. It may be through interoperation with cloud and blockchain technology that AI's capabilities will be fully harnessed.

Artificial intelligence is becoming increasingly popular. More and more businesses are adopting AI approaches and implementing the use cases that are efficient enough, the global revenue of AI solutions is estimated to reach the mark of 118.6 billion dollars by 2025. Serving customers and businesses with the best experience and services worldwide, AI approaches are rapidly replacing the traditional approaches and decreasing the requirement for humans to perform certain redundant tasks. One of the most popularly known and used concepts of AI (Artificial Intelligence) is chatbots. Chatbots are built to serve the customers by answering their queries efficiently and on time without needing a human from the business side.

CATONSVILLE, MD, September 23, 2019 - Chatbots, which use artificial intelligence to simulate human conversation through voice commands or text chats, incur almost zero marginal costs and can outsell some human employees by four times, so why aren't they used more often? According to new research, the main contributor is customer pushback. The machines don't have "bad days" and never get frustrated or tired like humans, and they can save money for consumers, but new research in the INFORMS journal Marketing Science says if customers know about the chatbot before purchasing, sales rates decline by more than 79.7%. The study authors, Xueming Luo and Siliang Tong (both of Temple University), Zheng Fang of Sichuan University, and Zhe Qu of Fudan University, targeted 6,000 customers from a financial services company. They were randomly assigned to either humans or chatbots, and disclosure of the bots varied from not telling the consumer at all, to telling them at the beginning of the conversation or after the conversation, or telling them after they'd purchased something.

Keep your Amazon Echo close to your bed for when you really need it. When you wake up feeling groggy and sick, the last thing you want to do is get out of bed and go see the doctor. Fortunately, if you've got your Amazon Echo ($70 at Amazon) at your side (or even the Alexa app), you can get diagnosed right from your comfy zone. While Alexa isn't a doctor and can't physically examine you, it can use the web and its smarts to help give you a diagnosis based on the condition you've described. Not to mention, you can avoid that dreaded copay and doctor bill.

Google's Pixel phones are the company's preferred way of showcasing its AI chops to consumers. Pixel phones consistently set the phone camera bar thanks to Google's AI prowess. But many of the AI features have nothing to do with the camera. The Pixel 4 and Pixel 4 XL unveiled this week at the Made by Google hardware event in New York City continue this tradition. Camera improvements aside, the Pixel 4 makes a play for a new arena that Google clearly wants to rule: offline natural language processing.

The global artificial intelligence and education Market is significantly driven by the integration of intelligent algorithms as well as Advanced Technologies in to e-learning platforms. Education software, machine learning, and artificial intelligence are some of the Innovative learning models and Technologies change the rules and creating tremendous shift from the teaching methods. These technologies have completely transformed with a classroom. The sophistication level has increased tremendously with the increasing adoption of artificial intelligence and machine learning algorithms. These Technologies are becoming extremely useful for developing user-friendly decision support systems and used in knowledge acquisition applications, language translation, and information retrieval.

Artificial intelligence (AI) is already re-configuring the world in conspicuous ways. Data drives our global digital ecosystem, and AI technologies reveal patterns in data. Smartphones, smart homes, and smart cities influence how we live and interact, and AI systems are increasingly involved in recruitment decisions, medical diagnoses, and judicial verdicts. Whether this scenario is utopian or dystopian depends on your perspective. The potential risks of AI are enumerated repeatedly.

Google held its big annual hardware event Tuesday in New York to unveil the Pixel 4, Nest Mini, Pixelbook Go, Nest Wifi, and Pixel Buds. It was mostly predictable because details about virtually every piece of hardware the company revealed at the event were leaked months in advance, but if Google's biggest hardware event of the year had an overarching theme, it was the many applications of on-device machine learning. Most of the hardware Google introduced includes a dedicated chip for running AI, continuing an industry-wide trend to power services consumers will no doubt enjoy, but there can be privacy implications too. The new Nest Mini's on-device machine learning recognizes your most commonly used voice commands to quicken Google Assistant response time compared to the first-generation Home Mini. In Pixel Buds, due out next year, machine learning helps recognize ambient sound levels and increase or decrease sound the same way your smartphone dims or brightens when it's in sunlight or shade.