The challenge is that the math behind it is somewhat complicated, and that it has to be run, over and over, across vast quantities of data to suss out the statistical weights and biases of a particular system. The work will get done; it might just take a long time. Data scientists and machine learning researchers have long used graphics processing units (GPUs) because of their highly parallelized architecture and relatively abundant on-chip memory available. But as industry and research groups alike seek more efficiency and need to accommodate ever-larger quantities of information, more specialized computing hardware is required for the task. Headquartered in Bristol, U.K., Graphcore is in the business of producing silicon purpose-built for munching through machine-learning math at high rates of speed and using less electricity than GPUs. Benchmarks for Graphcore's Intelligence Processing Unit (IPU) state that it offers notably less latency and higher computational throughput, and uses less power than GPUs.

Artificial intelligence, in its many forms, will likely continue to redefine how individuals think about government, school, work, and daily life. And startups that are not leveraging AI are beginning to fall behind the rest of the pack. "AI will be a true differentiator for companies in the future. The companies that have mastered it will take off, those who haven't will dwindle (just like companies choosing to embrace software and the internet early on)," Scott Stephenson, founder and CEO of Deepgram, an automatic speech recognition company, told Crunchbase News in an email. And in a world where supergiant rounds are now quite common, AI startups have brought in some significant capital.

In late May, Chinese facial recognition technology developer SenseTime Group announced it had raised $620 million in a second round of funding. The raise valued the company over $4.5 billion, making it the world's most valuable AI unicorn. SenseTime reportedly became profitable last year. Meanwhile, UiPath--an enterprise robotic process automation (RPA) software company--brought in $153 million in a massive Series B round led by previous investor Accel. UiPath said the round pushed it into unicorn status and followed a record year of growth.

Travel planning can be time consuming and frustrating. With constantly fluctuating prices, securing the cheapest flights and hotels seems more like an urban myth. But it's not just big providers that may struggle with setting the right price. Airbnb hosts face the same uncertainty in how to set flexible listing prices in response to changing market demands. The fancy economic term for this dilemma is "dynamic pricing," and several startups, along with Airbnb itself, have tapped into the market. One of the startups working to bring dynamic pricing to the masses, Wheelhouse, builds its pricing model on "over two dozen factors," according to its help page.

At some point in the future, while riding along in a car, a kid may ask their parent about a distant time in the past when people used steering wheels and pedals to control an automobile. Of course, the full realization of the "auto" part of the word -- in the form of fully autonomous automobiles -- is a long way off, but there are nonetheless companies trying to build that future today. However, changing the face of transportation is a costly business, one that typically requires corporate backing or a lot of venture funding to realize such an ambitious goal. A recent funding round, some $128 million raised in a Series A round by Shenzhen-based Roadstar.ai, In short, not as many as you'd think.

It has been a good week for artificial intelligence companies. This morning, Crunchbase News reported that Beautiful.AI, an AI-powered presentation software, raised an $11 million Series B to end frustration with PowerPoint. Similarly, Alloy announced today that it has closed a $12 Million Series A round for its AI-powered supply chain platform. This round brings its total known funding to $15.3 million, according to its press release. Alloy was co-founded by its CEO Joel Beal with the goal to help companies use data with insights about supply and demand to inform business decisions.

Crunchbase News has covered the U.S. FinTech space a great deal since the start of the quarter. From looking at FinTech's early-stage stars of 2017 and 2018 to NYC's growing fintech scene, to traveling down south to Atlanta, which has a FinTech startup scene of its own. To get a hold on the space in Q1 2018, we take a look at venture dollar invested into U.S.-based FinTech startups quarter by quarter since 2016. Crunchbase News's definition of FinTech encompasses a broad range of startups that leverage deep learning technology and big data in order to streamline tax processes, make paying friends easier, and provide better insurance options.1 Since 2016, more than $15.6 billion has been invested in seed, early, and late-stage U.S.-based FinTech startups. Total dollar volume saw an increase of 25 percent year over year from 2016 to 2017.

Alarmist headlines abound about how AI and robots are stealing jobs from humans. The fears are spreading beyond concern over blue-collar jobs. Now there are greater worries that even white-collar jobs that require additional training and education will be replaced by artificial intelligence and AI-powered robots. Crunchbase News turned to AI expert, SparkCognition CEO and Founder Amir Husain, to get his thoughts on the matter. Austin-based AI startup SparkCognition has raised $56.5 million over the past year and $73.5 million since its inception in 2013.

A few years ago, Teslas driving down Sand Hill Road couldn't kick up a pebble and somehow not hit a founder--typically fresh out of a PhD program--talking about their new machine learning startup. "Machine learning" was the "blockchain" of its day. However, it appears as though venture funding into companies specifically focused on machine learning and AI and its applications has begun to level off, at least in the US. But that may not be such a bad thing. Before we discuss why that could be the case, let's take a look at the numbers.

Artificial intelligence has become quite the buzzword in the past year. Salesforce Ventures and Bloomberg Beta have built specific funds targeting the category. Meanwhile, exits are positive for AI startups, courtesy of major tech companies looking to bolster internal AI efforts. But while tech startups are embracing the AI terminology in their marketing materials and VC pitch decks, the technology often claimed to be AI is actually smorgasbord of machine learning, advanced algorithms (also known as deep learning), and natural language processing--with a dose of humanity to help tape it all together. And it's that last bit, the human bit, that founders and investors tend to gloss over. After all, the whole point of artificial intelligence is to make our gelatinous brains unnecessary.