Machine learning is, as simple search for news can prove, a hot topic in the tech industry. Its varied applications make machine learning truly unique: from the existing ones, such as disease prediction or loan assessment, to future applications, like generating videos from photos or identifying pixelated faces, the list is virtually endless. Another important application of machine learning has to do with Internet security. More specifically, an issue that affects the mobile advertising industry: fraud. The rise in fraud is one of the greatest challenges currently that mobile video advertising has to face these days, as bots and other sources can have a negative impact on the advertisement's effectiveness.

This is another installment of Spare5's "Conversations in Machine Learning" blog series. Each week, our content human, Cassie, shares a summary of a recent conversation we had with a machine learning team and potential customer--what they're building, how they're handling training data today, etc. Read more about the series here. Shut up and take my money, is basically how I feel about this week's rad application of machine learning. We've had a few calls recently with the Research Engineer and Head of Research at a company that made us all at Spare5 go, "Oh I love them! Coooool!" when we found out they'd come into our prospective-customers universe.

It's just the way it is: the human being is a lazy creature. If it's possible not to do something, we don't do it. However, paradoxically this is a good thing, because laziness is… the engine of progress! Well, if a job's considered too hard or long-winded or complex for humans to do, certain lazy (but conscientious) humans (Homo Laziens?:) give the job to a machine! In cybersecurity we call it optimization.

AI is the cornerstone of the next generation of technology applications and is already on its way to infiltrating every part of our daily lives. As such, training AI from a diverse set of perspectives on the world is vital if we hope to advance AI in a way that makes the world a better place. The ever-important task of fostering diversity in the burgeoning AI community is a responsibility that falls upon all of us, not just corporate gatekeepers or select data scientists with advanced technical degrees. Matt Zeiler unveils groundbreaking new technologies that will transform the way AI is "taught" and make both teaching and using AI accessible to anyone in the world.

As Artificial Intelligence race is on, major tech companies are already developing Chatbots to serve their customer in a better way. Many customer services oriented businesses believe that Artificial Intelligence tool could help their companies. But are not sure if their business is sophisticated enough to implement Chatbots in their systems. While there are some imperatives for implementing an AI-based virtual assistant in your organisation, the entry barrier is much lower than many believe. Chatbots and Service Industry can go together till long extend to solve customer queries efficiently saving human cost and giving customers a pleasant and personalised experience.

Deep learning sounds daunting, but it's fast becoming a necessary technology in today's contextual world. Whether you're building simple bots or larger neural networks, a good understanding of artificial intelligence will help you succeed in your endeavor. If you're not even sure what deep learning is, you're definitely not alone. At its core is the building and programming of neural networks that allow machines to decipher speech or text to suit various needs. The best expression of deep learning comes with digital assistants such as Siri or Google Now.

I've been writing code on and off for a long time and used a variety of different languages and supporting technologies. From my perspective, there's been two distinct cycles of development technology during my time in the industry. I started out programming using 3GL's such as C and Pascal. On top of those languages we added ever increasing layers of abstraction to accelerate the development process. Ultimately we ended up using 4GL's such as PowerBuilder and Visual Basic.

The rise of the machines is coming to the sales process. Earlier this week – just hours before the kickoff of Oracle's annual conference OpenWorld – Salesforce unveiled Salesforce Einstein, an artificial intelligence (AI) solution built to work seamlessly across Salesforce platforms. The introduction of AI to the Salesforce ecosystem provides a wide range of uses for sales and marketing teams. But how, specifically, can sales leaders and their teams leverage AI to improve sales team performance and the customer experience? And how eager should sales leaders be about incorporating AI into the sales process?

Artificial intelligence programs, the Internet of Things, and next-level big data analytics tools are likely to start producing a significant impact on healthcare delivery as early as 2017, say participants in a new Silicon Valley Bank survey. The poll, which includes responses from 122 health IT company founders, executives, and investors attending a recent event, indicates a general belief that big data will continue to be a primary driver of innovation in the healthcare industry, but may run into adoption challenges and regulatory hurdles in the near future. Forty-six percent of participants said that big data will have the greatest impact on healthcare over the next year, followed by 35 percent who believe artificial intelligence (AI) will be a major game-changer. AI tools may have a broad range of applications across the healthcare spectrum, including patient engagement and customer relations, chronic disease management, clinical decision support, and sophisticated big data mining and analytics for diagnostics, population health management, and financial modeling. Many current attempts to develop artificial intelligence for healthcare are based on semantic computing or cognitive computing techniques that require vast stores of big data to fuel algorithms that try to mimic the complexity and predictive capabilities of human thought.

If you're a software engineer (or someone who's learning the craft), chances are that you've heard about deep learning (which we'll sometimes abbreviate as "DL"). It's an interesting and rapidly developing field of research that's now being used in industry to address a wide range of problems, from image classification and handwriting recognition, to machine translation and, infamously, beating the world champion Go player in four games out of five. A lot of people think you need a PhD or tons of experience to get a job in deep learning, but if you're already a decent engineer, you can pick up the requisite skills and techniques pretty quickly. Important point: You need to have motivation and be able to code and problem solve well. Here at Deepgram we're using deep learning to tackle the problem of speech search.