Artificial intelligence is a moving target. Here's how to take better aim. Artificial intelligence (AI) seems to be everywhere. We experience it at home and on our phones. Before we know it--if entrepreneurs and business innovators are to be believed--AI will be in just about every product and service we buy and use. In addition, its application to business problem solving is growing in leaps and bounds. And at the same time, concerns about AI's implications are rising: we worry about the impact of AI-enabled automation on the workplace, employment, and society.

As the artificial intelligence field continues to grow, businesses across the country have found that techniques are coming out of the research lab and into the applied realm to benefit their operations. Recently, Boston Medical Center implemented predictive analytics into its system to determine staffing during the hospital's busiest hours. Armed with this information, the center is able to adequately staff various areas of the hospital to ensure patients receive timely treatment. This technology doesn't just stop the hospital from being short-staffed -- it drastically improves the efficiency and response time for every patient. Netflix and other entertainment sites commonly take advantage of this technology by suggesting shows for users to watch based on a variety of behavioral factors.

The dataset consists of 279 folders, with sub-folders 0 and 1 inside each of the 279 folders. We then create a function process_images which takes as input the starting and end index of the images. This function first reads the image using OpenCV's cv2.imread() and also resizes the image. Resizing is done because few of the images in the dataset are not 50x50x3. The function returns two arrays: X, which is an array of the resized image data and Y, which is an array of the corresponding labels.

Machine learning and in particular deep learning revolutionize the world as we know it today. We have seen tremendous advances in speech and image recognition, followed by application of deep learning to many other domains. In many of those domains, deep learning is now state of the art or is even going beyond it. A clear trend is that networks are growing more and more complex and more and more computationally demanding. Today, we are building ever increasing networks that are built on top of previous generations of network topologies.

To attack this problem, our research team developed a patent-pending framework that uses Deep Learning to automatically generate a "voice fingerprint" for each sales rep using a combination of vocal characteristics. During the sales call itself, we cluster the audio signals based on those characteristics with each cluster representing a speaker. The voice fingerprints we stored play a crucial role not only in associating each speaker with the right cluster, but in the clustering process itself: the models we trained with the fingerprints allow us to learn and apply mathematical transformations to the audio, which render the differences between different speakers more distinct. See the before and after graphs below.

There are several things holding back our use of deep learning methods and chief among them is that they are complicated and hard. Now there are three platforms that offer Automated Deep Learning (ADL) so simple that almost anyone can do it. There are several things holding back our use of deep learning methods and chief among them is that they are complicated and hard. A small percentage of our data science community has chosen the path of learning these new techniques, but it's a major departure both in problem type and technique from the predictive and prescriptive modeling that makes up 90% of what we get paid to do. Artificial intelligence, at least in the true sense of image, video, text, and speech recognition and processing is on everyone's lips but it's still hard to find a data scientist qualified to execute your project.

A report from Accentureclearly highlights those companies that don't want to capitalize on AI will not survive the future (see Figure 1). The AI war is being fought with open source technologies such as TensorFlow, Spark ML, Caffe, Torch and Theano. But wait…if these AI algorithms and technologies – the "Weapons of Mass Business Model Destruction" – are readily available to everyone, what are the sources of business value and differentiation? Increasingly the equation for deriving and driving business value and differentiation isn't having the Machine Learning, Deep Learning and AI frameworks, but is found in two foundational principles: I have already written the book "Big Data MBA: Driving Business Strategies with Data Science" and several blogs on the "Thinking Like A Data Scientist" methodology which provides a thorough, business-driven process for identifying, validating, valuing and prioritizing the organization's business and operational use cases. See the following blogs for more details on the "Thinking Like A Data Scientist" methodology: Now let's consider the other part of the AI equation – monetizing the data.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Last week, in an essay for The New York Times, famous mathematician Steven Strogatz praised the recently published performance results of AlphaZero, the board game–playing AI developed by DeepMind, a British AI company acquired by Google in 2014. While his examination of AlphaZero's findings is an interesting read, some of the conclusions Strogatz draws about the general advances in AI are problematic. "[AlphaZero] clearly displays a breed of intellect that humans have not seen before, and that we will be mulling over for a long time to come," Strogatz writes early in the article. Further down, Strogatz writes, "By playing against itself and updating its neural network as it learned from experience, AlphaZero discovered the principles of chess on its own and quickly became the best player ever."

Artificial Intelligence is a practice or rather a theory of making machines able to perform tasks as humanly as possible. To make AI work at its best, programmers need to make use of deep learning, neural networks, and machine learning as well. However, researches have shown a recent and growing trend. We need to integrate blockchain with Artificial Intelligence to achieve more efficiency and security.

You can read the post in Romanian here. The majority of advancement and business value of AI in 2018 was driven by Deep Learning (if the difference between Deep Learning and AI is not very clear, I recommend first checking out this post). Beside driving business value, Deep Learning combined with Reinforcement Learning had some funny achievements, being able to defeat a team of 5 professional Dota 2 players. The OpenAI team behind the development of this bot confirmed that the algorithm was learning and exploring the Dota world all by itself (more information can be found here). Let's explore a few important facts about the AI market for 2018, because "numbers don't lie": Even though some numbers are impressive, there is still room to grow. In 2018 many applications of AI were explored, some of them did not drive business value at all, they rather focused on algorithmic advancements and complexity.