If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Did you know that you can execute R and Python code remotely in SQL Server from Jupyter Notebooks or any IDE? Machine Learning Services in SQL Server eliminates the need to move data around. Instead of transferring large and sensitive data over the network or losing accuracy on ML training with sample csv files, you can have your R/Python code execute within your database. You can work in Jupyter Notebooks, RStudio, PyCharm, VSCode, Visual Studio, wherever you want, and then send function execution to SQL Server bringing intelligence to where your data lives. This tutorial will show you an example of how you can send your python code from Juptyter notebooks to execute within SQL Server.
Customer churn is a major headache for most companies and threatens to put the brakes on the red-hot growth of the pay-as-you-go (PAYGo) solar sector. With over 1 million units sold in the last 5 years and over 50,000 units installed each month, the PAYGo model makes solar affordable for end-users and provides sufficient margin for providers to scale last-mile distribution. However, for the model to succeed PAYGo operators must retain customers and build a base of loyal and engaged customers. Our project with Zola Electric (formerly Off Grid Electric) demonstrates that machine learning can help them do so. PAYGo operators make money from installments and/or fees as end-consumers pay off solar assets over 1 to 3 years.
In April and May this year, we hosted two meetups (this and this) that outlined considerations in building products based on artificial intelligence (and machine learning) from a product management point of view. Artificial intelligence (AI) became popular in the 1980s (Wikipedia), but its recent boom is mostly due to machine learning: the idea that one can use historical data to predict future behavior. One example in the Gong world is a task called punctuation restoration. Historically, speech-to-text systems convert audio into words, but do not include punctuation (periods, commas, and the like).
Careers in data science, artificial intelligence, machine learning, and related technologies are considered among the best choices to pursue in an uncertain future economy where many jobs may end up automated and performed by robots and AI. Yet in spite of the likely strong and secure future of these careers, the job marketplace remains fundamentally unbalanced. There are still many more jobs open and available than there are qualified applicants to fill those jobs. Just do a search on Monster for the keyword machine learning and you will find thousands of job openings across the country. Whether you are just starting out in your IT career or you are watching high-profile IT layoffs and considering the best new skills to learn, chances are you are wondering what the best skills are to emphasize on your LinkedIn profile and the best skills to focus on in the next online course you take.
When we discuss about artificial intelligence (AI), how are machines learning? What kinds of projects feed into greater understanding? For our friends over at IBM, one surprising answer is movies. To build smarter AI systems, IBM researchers are using movie plots and neural networks to explore new ways of enhancing the language understanding capabilities of AI models. IBM will present key findings from two papers on these topics at the Association for Computational Linguistics (ACL) annual meeting this week in Melbourne, Australia.
IBM on Thursday said it's extending its partnership with the US Department of Veterans Affairs to apply artificial intelligence to cancer treatments for veterans. The VA and IBM Watson Health first partnered to help cancer patients in 2016, as part of then-Vice President Joe Biden's cancer moonshot initiative. The partnership uses the Watson cognitive computing platform to help the VA's precision oncology department deliver individualized treatment plans. So far, the VA has used IBM Watson to help more than 2,700 veterans with cancer. To prepare an individualized treatment plan, teams of scientists and clinicians must sequence a patient's DNA to pinpoint the likely cancer-causing mutations and determine what treatments would target those specific mutations.
To kick off a series of Neo4j extensions for machine learning, I implemented a set of user-defined procedures that create a linear regression model in the graph database. In this post, I demonstrate use of linear regression from the Neo4j browser to suggest prices for short term rentals in Austin, Texas. Let's check out the use case: The most popular area in Austin, Texas is identified by the last two digits of its zip code: "04". With the trendiest clubs, restaurants, shops, and parks, "04" is a frequent destination for tourists. Suppose you're an Austin local who's going on vacation.
One of the biggest challenges with deep learning is explaining to customers and regulators how the models get their answers. In many cases, we simply don't know how the models generated their answers, even if we're very confident in the answers themselves. However, in the age of GDPR, this black box-style of predictive computing will not suffice, which is driving a push by FICO and others to develop explainable AI. Describing deep learning as a black box is not meant to denigrate the practice. After, all, we're thrilled that, when we build a convolutional neural network with hundreds of input variables and more than a thousand hidden layers (as the biggest CNNs are), it just works.
It's been one year since my team and I began our journey into the online accounting ecosystem. I've been reflecting on our key observations and learnings since we started building our company. I'd be doing our bookkeeper and accountant (B/A's) partners a disservice if I didn't share some thoughts on technology advancements. On a day-to-day basis technology continues to disrupt both personal and professional lives. Many professions are forced to reimagine their business models or eventually become obsolete.
What is Machine Learning, Data Science or Artificial Intelligence? is one of the most common questions which I have faced from people. Be it newcomers, recruiters or even people in leadership positions, this is a question which is puzzling everyone in its own way. For beginners it takes the form of how do I become a data scientist? For leaders it becomes a question of whether it has an imperative business impact? This post is an attempt to clear some of the myths and develop a basic understanding around what Data Science is, and its different interpretations in corporate world.