Forecasting is a critical part of business operations. For example, being able to estimate future customer demand and sales numbers can help companies anticipate revenue. Similarly, technology departments have to predict ongoing storage needs, computing requirements, and other areas that fall in their purview while planning departments have to prepare for the number of products that may be needed to meet demand.
The ability to forecast accurately is often deemed critical. Without reasonable numbers, operational decisions may not align with what actually occurs over the next weeks, months, or years.
Often, companies rely heavily on people to make forecasts. However, as machine learning and artificial intelligence (AI) become more accessible, we are on the precipice of a forecasting transformation; one that has already started to take root.
If you are wondering how machine learning and AI are improving forecasting, here’s what you need to know.
In Silicon Valley, the IT job market is hypercompetitive. Startups hoping to be the next Google, Facebook, or Snapchat use a wide range of tactics designed to give them a shot at snagging top engineering talent — the skills that can make or break a technical company, and mean the difference between billions and bust.
But the latest play in the engineering talent wars, being launched by startup Weeby.com, embraces a radically different philosophy from typical Valley tech startups. Instead of luring in talent with the promise of world-changing tech and substantial equity that will theoretically make them millionaires if their hard work pays off, Weeby.com is offering to make engineers millionaires from the start — by paying them a million dollars for their first four years of work.
The strategy: A transparent and “backwards” pay structure
Weeby’s salary structure represents a near-complete reversal of traditional Silicon Valley startups. While other companies establish ultra-low startup salaries and rely on finding passionate engineers who believe in the founder’s vision, Weeby.com intends to pay their talent like they’re already superstars, right from the gate.
The company’s founder, Michael Carter, believes that even the average market range salary of $111,000 for engineers in Silicon Valley isn’t enough. The Valley is one of the most expensive real estate markets in the world, and employees at a very low six-figure income still worry about making mortgage payments and raising families. At usual startup salaries, which can run $50,000 to $75,000 plus equity, those worries become serious concerns — and drive top talent straight to higher-paying doors like Facebook and Google.
The restructured compensation at Weeby begins with a base salary that’s at least $100,000 and commensurate with experience — always more than what engineers were previously paid. Engineers are then given performance-based monthly bumps of $10,000 until they reach $250,000. At that point, the monthly raises continue on a smaller scale, but ultimately the salary amounts to $1 million in four years.
In addition, Weeby is offering up to four times more equity than Silicon Valley startups of similar size, in a structure that will have employees collectively owning more of the company than its biggest investor.
The opposition: Higher salaries will attract mercenaries
Not everyone in Silicon Valley agrees that paying engineers higher-than-market rates is a smart idea, especially for startups. In an interview with CNet, Y Combinator president Sam Altman called the strategy “a horrific idea,” saying that if a company is known for paying huge cash salaries, they’ll end up attracting terrible cultural fits. Altman adheres to a more traditional view, stating that startups should recruit an initial batch of core employees who are “maniacally dedicated” to the company’s vision and products, and believe they’re working for a purpose that is bigger than themselves.
Three-time Silicon Valley founder Steve Newcomb, in the same interview, asserted that paying exorbitant salaries can harm a startup company’s reputation before they get off the ground. “If you have to pay people more money than market to come work for your company, then that’s a statement of the value of your product and the value of your company,” Newcomb said — also mentioning that above-market salary investments could upset investors.
However, Weeby’s investors are on board with the strategy, including Karl Jacob, who served as an advisor to Mark Zuckerberg’s six-man board during Facebook’s early days. Carter hopes that the idea of paying top engineers what they’re truly worth will spread, and more Valley startups will be able to build superstar teams that can change the world — and still get paid.
“Silicon Valley’s about getting a great team together and trying new things,” Carter said. “When you do something for the first time, it allows you to approach something with a fresh eye, [and] sometimes, you get a result like Google, Facebook, or Snapchat.”
As technology continues to advance rapidly, the machines we use are getting smarter. Machine learning is the technology of constructing “learning” algorithms that drive a broad range of smart technologies — and the new generation of this discipline, called deep learning, has the potential to power more advanced artificial intelligence capable of everything from sophisticated speech and image recognition, to self-driving cars.
What is deep learning?
Deep learning, also called deep structured learning or hierarchical learning, is a type of machine learning that uses high-level data abstractions, nonlinear transformations, and layered cascades applied to learning representations of data, in order to help machines “learn” tasks through observations and examples.
Algorithms with deep learning applied are often inspired by communication patterns found in neuroscience — the study of the human nervous system. For example, a deep learning algorithm might be based on the relationship between a stimulus and a neural response, which registers as electrical activity in the brain. This type of machine learning attempts to create neural networks for machines that “think” in ways similar to humans.
Following are a few of the applications currently being developed with deep learning algorithms.
Automatic speech recognition
Technologies such as Apple’s Siri are built on machine learning algorithms that work to recognize speech, including words and sounds. Deep learning has led to the advancement of automatic speech recognition using the TIMIT data set — a limited-sample database using 630 speakers and eight major American English dialects, each with 10 different spoken sentences — to large vocabulary speech recognition through DNN models that rely on deep learning algorithms.
Deep learning differentiates from other forms of machine learning through the use of raw features at a learning level, rather than pre-constructed models. With deep learning, speech recognition can be highly accurate using the true “raw” form of speech — waveforms, or visual representations of sounds using curves.
Similar to speech recognition, a limited size data set called the MNIST database has been the popular model for powering image recognition applications. This database includes 60,000 training examples and 10,000 test examples, composed of handwritten digits. However, MNIST relies on shallow machine learning for image recognition — and deep learning allows for more large-scale image recognition at a higher accuracy rate.
One practical example of deep learning algorithms applied to image recognition can be found in the automotive industry. A car computer trained with deep learning may enable cars to process and interpret 360-degree camera views, allowing for heightened “awareness” in self-driving or assisted-driving vehicles.
Many in the tech industry view deep learning as a strong step toward realizing truer artificial intelligence. In 2013, Google hired three DNN researchers tasked with not only dealing with the search engine giant’s constantly growing stores of data, but also to improve Google’s existing machine learning products, such as semantic role labeling and search results.
Facebook has also created an artificial intelligence lab, largely dedicated to the development of deep learning techniques that will improve the user experience. Automatic image tagging was developed in Facebook’s AI lab — a technology that is still being refined for greater accuracy using deep learning.
As machine learning continues to increase in sophistication, more companies will look to hire IT professionals interested in developing deep learning algorithms and improved artificial intelligence applications. Machine learning is an exciting field with a wide range of possibilities ahead.