You were pretty stressed yesterday. Are you feeling better today?" What might sounds like a concerned message from friends or parents is actually a query from Replika, a chatbot. "If you're feeling down, or anxious, or you just need someone to talk to, your Replika is here for you 24/7," the company behind the chatbot writes on its website. Chatbots – a combination of "chat" and robot" – are programs that simulate a conversation, usually by text message.

Amazon's naked ambition to become part of everyone's daily lives was on full display this week at its annual hardware event. It announced a slew of new Alexa-powered devices, including a home surveillance drone, a suite of Ring-branded car alarm systems, and miscellany like an adorable little kids' Echo device. But it's clear Amazon's strategy has shifted, even if only for a product cycle, from going wide to going deep. Last year, Amazon baked its virtual assistant into any household device that could accommodate a chip. Its list of new widgets with Alexa seemed a mile long and included a menagerie of home goods, like lamps and microwaves.

Companies like Mastercard are implementing AI strategies that are transforming how customer experience is done. Join this VB Live event for insights on why AI is essential for fintech companies, plus how to implement it, how to make it perfom, and more. AI has been around for a long time -- it's only in the last three or four years that people have been paying attention to it in the fintech space, says Dr. Steve Flinter, VP of artificial intelligence and machine learning at Mastercard Labs. "A huge driver of innovation is that small startups, fintechs, and non-technology corporates are able to get access to this technology that five or 10 years ago would have been locked away in university research labs and the big corporate R&D labs," Flinter says. The amount of data now available, and the ability to store and process that at scale, combined with open source technology, compute power, and breakthroughs in technologies like computer vision and NLP are all part of this AI democratization.

Advances in artificial intelligence depend on continual testing of massive amounts of data. This benchmark testing allows researchers to determine how "intelligent" AI is, spot weaknesses and then develop stronger, smarter models. The process, however, is time-consuming. When an AI system tackles a series of computer-generated tasks and eventually reaches peak performance, researchers must go back to the drawing board and design newer, more complex projects to further bolster AI's performance. Facebook announced this week it has found a better tool to undertake this task--people.

However, our bot didn't "know" anything about "Chitra" or Tagore. It didn't generate fundamentally new ideas or sentences. It simply cobbled together parts of existing sentences from existing articles to make new ones. OpenAI, a for-profit company under a nonprofit parent company, has built a language generation program dubbed GPT-3, an acronym for "Generative Pre-trained Transformer 3." Its ability to learn, summarize, and compose text has stunned computer scientists like me. "I have created a voice for the unknown human who hides within the binary," GPT-3 wrote in response to one prompt. "I have created a writer, a sculptor, an artist.

As the capabilities of artificial intelligence (AI) are growing, insurers are finding new ways to capitalize on this technology. Here are some of the most prominent advantages of Insurance companies adopting AI. IoT (internet of things) refers to the interconnection of physical objects via the internet. These devices--things--are embedded with software and sensors for the purpose of connecting and exchanging data with other systems and devices over the internet. The internet of things includes devices such as Google Home and Amazon Echo.

Offered through a collaboration with Microsoft, this microcredential will teach you the fundamentals of AI and provide you with the skills to design and build an AI solution using Microsoft Azure. We will prepare you for the Microsoft Azure Fundamentals (AZ-900) and Microsoft Azure AI Engineer Associate (AI-100) certification; the cost of this microcredential includes vouchers for those exams. Artificial intelligence is one of the key drivers of the Fourth Industrial Revolution. Accordingly, artificial intelligence skills are frequently listed among the most in-demand workplace skills in the current and future job market, as organisations seek to harness AI to revolutionise their operations. While in-demand tech skills are changing, employers are faced with a shortfall of qualified candidates.

Conversational artificial intelligence, natural language processing and voice could be the next disruptive technologies in the financial services industry, but there continue to be barriers to advisors and clients of theirs and other financial institutions accepting these technologies, according to Kyle Caffey, managing director of conversational AI at Charles Schwab, and Swapna Malekar, product lead at RBC. "Financial use cases can be more complicated, and so delivering a good client experience could certainly be a part of that adoption barrier," Caffey said last week while speaking during a "Future Focus" session at the Finovate Fall Digital online conference. Another big issue is that "the nature of the data that we're relying on and passing through some of these technologies is sensitive," he said, underscoring the importance of risk management. Schwab has been careful with privacy and the "security of our clients' data as we pursue some of these technologies -- and that's something that we've been very mindful [of] as we've executed on our vision and road map, and it's something for any financial services firm or bank that's considering deploying conversational AI … to make sure they're paying attention to," he said. In most cases, business use cases for technology are "built on either a revenue plan or an expense savings plan," he noted, adding: "Certainly, conversational AI brings to bear capabilities on both sides of the business case." But Schwab "really started with the client experience and thought about'how do we make our digital ecosystem as accessible and simple for our clients [as possible] to get them what they're looking to get done?'"

To build a machine learning model dataset is one of the main parts. Before we start with any algorithm we need to have a proper understanding of the data. These machine learning datasets are basically used for research purposes. Most of the datasets are homogeneous in nature. We use a dataset to train and evaluate our model and it plays a very vital role in the whole process. If our dataset is structured, less noisy, and properly cleaned then our model will give good accuracy on the evaluation time. Imagenet dataset is made by the group of researchers and the images in the dataset organized according to the WordNet hierarchy. This dataset can be used for machine learning purposes and computer vision research fields as well.

Amazon is making Alexa smarter with natural turn-taking, having conversations with multiple people, natural language understanding, and the ability to be taught by customers. The first target is the smart home, but Alexa for Business is also likely to follow. Also: When is Prime Day 2020? The Alexa overhaul and artificial intelligence improvements were outlined as Amazon launched its latest batch of Echo devices. Amazon's new Echo devices are evolving to be more smart home edge computing devices.