Emerging areas, such as machine learning, artificial intelligence (AI) and big data, require special skill sets in high demand. Beyond traditional four-year degrees and time-intensive training programs, the alternative paths to developing those skills are limited. The learning required is not something that can be accomplished through a Netflix or YouTube-style exploration of a catalog of videos. Training in machine learning or AI requires deeper, more structured learning and commitment.
As AI moves beyond proof-of-concept and sandbox implementation, companies are looking to recruit top machine learning talent, cultivate AI skills across their workforce, and begin to use this amazing set of technologies for incredible outcomes. There’s just one problem. There’s still not enough AI experts out there to make this a reality – and a huge AI skills gap is opening up as a result. Continue reading “Enabling companies to bridge the Artificial Intelligence gap with Nanodegree programs”→
Udacity Artificial Intelligence and Data Industry Advisory Board
As we look forward into a future we know will be shaped by the transformational impact of artificial intelligence and data technologies, we can clearly see the birth of a new knowledge ecosystem within which education, industry, and technology form a powerful partnership. That these three arenas will be interrelated goes without saying, but how they inform one another, and how these relationships take shape and evolve, remain open questions.
At Udacity, we recognize the singular role we occupy, existing as we do at the crossroads where education, industry, and technology meet. We are a learning provider that teaches AI and data skills, in partnership with industry, and as such, we see a unique opportunity—and feel a special obligation—to both facilitate and contribute to the global conversation around critical issues we face as we move into our AI and data-powered future.
We are very excited to have recently formed an Artificial Intelligence and Data Industry Advisory Board with the expressed goal of bringing together leading experts in the field to consider the opportunities that lay ahead, to address the challenges we face, and to answer the questions we must answer.
We believe that through combining experiences and skills, sharing insights and ideas, and producing solutions and strategies, we can lay out a plan for the future that is beneficial to all—a plan that nurtures and supports emerging generations of learners to master artificial intelligence and data skills, encourages and incentivizes industry to adopt beneficial AI and data practices, and guarantees a pipeline of highly skilled individuals who are committed to social good ideals, and the ethical adoption and implementation of transformational technologies.
Among the experts who have joined our board is Armen Pischdotchian, the Academic Tech Mentor at IBM. In his role, he mentors university faculty and students, and conducts enablement sessions—both in and outside of the company—pertaining to the IBM Watson Solution offerings. Here is Armen on why he wanted to be a part of the board:
“I strongly believe that the Advisory board, at its core, is addressing a gap that needs to be erased, and that is the space between industry and education. Udacity has the unique pedigree of listening to the needs of tech giants and startups and asking the question, what does your candidate need to be proficient so the firm will succeed?”
Armen is joined by an incredible roster of individuals who come to us from leading organizations such as Amazon, Google, NVIDIA, and more. It is with both gratitude and excitement that we introduce the inaugural members of the Udacity Artificial Intelligence and Data Industry Advisory Board:
Armen Pischdotchian, Academic Tech Mentor, IBM
Brad Klingenberg, VP of Data Science, Stitch Fix
Bryan Catanzaro, VP of Applied Deep Learning Research, NVIDIA
Cyrus Vahid, Principal Deep Learning Solutions Architect, Amazon
Dan Becker, Head of Kaggle Learn
Derek Steer, CEO, Mode
Jeff Feng, Product Lead, Data, Airbnb
Joe Spisak, Product Manager – Artificial Intelligence at Facebook
Jon Francis, VP of Customer Marketing Analytics & Optimization, Starbucks
Josh Gordon, Developer Advocate for TensorFlow, Google
Mike Tamir, Head of Data Science Uber ATG & Data Science Faculty member at University of California at Berkeley
Warren Barkley, GM, AI and Research, Microsoft
While each of these individuals brings to the board a wholly unique set of experiences and insights, they are united by a shared passion for learning, and for building a better future through the beneficial use of transformational technologies.
Our mission is to provide companies and their employees with meaningful opportunities to master valuable and in-demand skills. Jeff Feng is the Product Lead for Data at Airbnb, where he leads a team building machine learning infrastructure, data infrastructure, data visualization tools, and their experimentation platform. Here is Jeff on the passion that drives his participation:
“Shaping how people and machines make decisions with data is one of the most critical skills needed in the workforce over the next decade. Thus, providing learners with the practical knowledge needed to work with data is an area I am hugely passionate about.”
We look very forward to sharing more updates about the work of the board, and to furthering our engagement with the important issues and incredible opportunities before us. As we advance our efforts, we are thankful above all else to our board members for their spirit of generosity and goodwill, and for their commitment to the true ideals of education. Josh Gordon, Developer Advocate at Google, put it both perfectly and simply when he stated the following:
“Good teachers are hard to find. I’m grateful for those who helped me out over the years, and it’s always been important to me to give back.”
We are grateful to the members of the advisory board, and we are excited to transfer insights gleaned from their leadership to you, our students, for it is who are the emerging leaders that will define the future we are eagerly building towards.
For more information about how Udacity for Enterprise is helping companies transform their workforce, click here.
Answers to some of the most commonly asked questions about AI
Artificial Intelligence (AI) is the world’s most exciting frontier for knowledge and technology. Everywhere you look, people are talking about intelligent machines improving our lives. For all of the excitement, many of the concepts and applications are still highly technical, and can be confusing if you’re not familiar with the basics of AI. If you, your company and your employees have questions, you certainly aren’t alone!
Read on to learn answers to some of the most commonly asked questions about AI.
What is Artificial Intelligence?
This is an important first question; here are key definitions everyone should know:
Artificial Intelligence
Artificial Intelligence is a branch of computer science focused on building computers and machines that can simulate intelligent behavior. Artificial Intelligence systems are able to perform tasks traditionally associated with human intelligence, such as visual perception, speech recognition, decision-making, and translating languages.
Algorithms
An algorithm is a series of mathematical instructions created for a machine to follow. Think of it as simple step-by-step instructions: do A, then B, then C. In AI, programmerscreate algorithms that tell a computer to look at data, identify a problem, and learn from its attempts to solve the problem.
Machine Learning
Machine learning is one of many algorithms used in AI. The machine learning field is concerned with designing programs that learn to make predictions from data, alone, without requiring assistance from a programmer. These algorithms are used in applications such as music recommendations, spam filtering, and fraud detection.
Deep Learning
Deep learning is built on neural networks, a kind of machine learning model structured in a way that resembles neurons in a human brain. In a neural network, artificial neurons are arranged in interconnected layers. There is an input layer to receive data from the outside world, and there is an output layer which dictates how the system will respond to the information. Between these two layers, there are additional “hidden” layers of neurons, which process data by putting a numerical weight on the information they receive from the preceding layer, and passing this information to the next layer in the network. A neural network can solve very complex problems because of the huge quantity of neurons working together. Deep learning gets its name from “deep” neural networks, with dozens or even hundreds of hidden layers. These networks are powering the AI revolution with state-of-the-art object detection, machine translation, and audio synthesis.
Natural Language Processing
Natural language processing is how we get computers to understand, process, and manipulate human language. To achieve this, a computer needs to be able to “understand” a huge amount of information—from grammar rules and syntax, to different colloquialisms and accents. In a speech recognition system, for instance, human voice input becomes audio data, which then gets converted to text data, a difficult process in itself. This text data can then be used in an “intelligent” system for various applications such as translators, or controlling devices like TVs.
Computer Vision
Computer vision is aimed at helping computers identify and process images in the same way humans do. Just as we learn to distinguish between the faces of different people, computer vision aims to teach machines to recognize different objects that it “sees” through a camera. It does this by looking at individual pixels, identifying different colors, and converting them to a numerical value, then looking for patterns so that it can identify groups of similarly colored pixels and textures. This helps it identify different objects.
Where is AI already being used?
AI is already present in many aspects of our lives. Examples include:
Smart Assistants. Smart assistants, such as Siri, Alexa, and Cortana, use natural language processing to understand voice commands—setting reminders, finding music, answering questions, even adjusting your thermostat—all from a home speaker or your smartphone.
Car “Autopilots.” Cars on the road today already use computer vision to operate a range of safety systems—such as tracking traffic around your car, and braking autonomously if the system perceives a danger ahead. To do this, the car needs to be able to rapidly identify different images, predict what could happen, and make a decision on what to do.
Recommending Purchases. Popular shopping websites use AI to track what you browse, what you buy, and what you save to look at later. It then uses this information to better tailor the products and services it recommends to you. As the customer, this saves you time searching for what you want. For retailers, it means being able to predict demand for products so they have the right stock in the right places. This improves delivery times and maximizes their chances they are able to sell you something you actually need.
Protecting your money. AI is used to constantly monitor bank accounts for potentially fraudulent activities. AI systems track all your purchases over time, and build a profile of your spending habits. The system can then rapidly flag any purchases that seem unusual. For example, if 99 percent of your purchases happen in your hometown, then suddenly a slew of purchases in another country show up, your bank can contact you to check if your card has been stolen.
Ride-sharing. Ride-sharing apps like Uber use machine learning to accurately predict when the car you book will arrive. When your app tells you that your driver will be arriving in three minutes, machine learning has been used to analyze the data from millions of previous customer trips to hone that prediction. AI techniques are also used to determine how many cars Uber needs to have on the road at any given time, and in what areas; for example, helping ensure there are extra cars around major stations at peak commuting times.
What AI developments are set to change the world?
Here are some of the most exciting AI developments experts expect to see in the future:
Fully automated transportation. AI will play a major role in the development of fully automated transportation systems—from self-driving cars to flying vehicles. Advanced AI systems will help vehicles react safely and intelligently to variable conditions such as other traffic, weather, and road conditions. This will result in transport that is much safer, quicker, and far less stressful than being in control ourselves. Autonomous transportation solutions will also reduce the amount of time people waste commuting through traffic, and free them up for more productive activities.
Taking over dangerous jobs. Some jobs are inherently dangerous—such as working with hazardous chemicals. As AI develops, robots with the capacity to make intelligent, independent decisions can take over these roles and remove the need for people to risk their lives doing them.
Faster and more accurate medical diagnosis. AI can help doctors increase the speed and accuracy by which they diagnose and treat medical conditions. Doctors will work with AI systems that can access a global database of medical conditions. The AI machine will compare patient symptoms with similar cases, and make recommendations almost instantly.
AI is poised to be the defining technology of the 21st Century. If you are ready to transform your workforce, provide critical upskilling for your teams, and gain competitive advantage, Udacity for Enterprise has a solution that is right for your organization.
