Think like a Human, But at Scale
What is MMS?
AI uncovers insights using Deep Learning neural networks not possible using traditional machine learning algorithms. with thousands of variables and non-linear patterns. The more data fed to deep-learning software, the better it continues to get. AI, particularly machine learning (ML) represents a fundamentally different approach to creating software. The machine keeps improving its performance without humans having to explain exactly how to accomplish all the tasks it’s given or being explicitly programmed for a particular outcome. It can figure out extremely complicated problems, with significantly less manual labor than traditional machine-learning methods.
As machines become increasingly capable, tasks considered as requiring “intelligence” are often removed from the definition, a phenomenon known as the AI effect. For example, optical character recognition is excluded from “artificial intelligence”, because it is now a routine technology. Capabilities generally classified as AI as of 2017 include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomous cars, intelligent routing in content delivery network and military simulations.
Just as the Internet has had some negatives, with cybercrime and loss of privacy, most people would find daily life significantly more difficult without it. In a similar way, AI will produce some negative consequences, but on balance, the benefits will outweigh the drawbacks.
How Artificial Intelligence Works
AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. AI is a broad field of study that includes many theories, methods and technologies, as well as the following major subfields:
- Machine Learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without explicitly being programmed for where to look or what to conclude.
- A Neural Network is a type of machine learning that is made up of interconnected units (like neurons) that relay information between each unit and process information in response to external inputs,. The process often requires multiple passes at the data to find connections and derive meaning from undefined data.
- Deep Learning uses huge neural networks with many layers of processing units. It takes advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Image and speech recognition are two common applications.
- Cognitive Computing is a sub-field of AI. It strives for a natural, human-like interaction with machines. Using AI and cognitive computing, the ultimate goal is for a machine to simulate human processes through the ability to interpret images and speech – and then speak coherently in response.
- Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings.
- Natural Language Processing (NLP) is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks.
Several Technologies Enable and Support AI
- Graphical Processing provides the heavy compute power that’s required for iterative processing. Training neural networks require big data plus compute power.
- The Internet of Things generates massive amounts of data from connected devices, most of it unanalyzed. Automating models with AI will enable greater use of the data.
- Advanced Algorithms are being developed and combined in new ways to analyze more data faster and at multiple levels. Intelligent processing is key to identifying and predicting rare events, understanding complex systems and optimizing unique scenarios.
- APIs, (Application Processing Interfaces) are portable packages of code that make it possible to add AI functionality to existing products and software packages. They can add image recognition capabilities to home security systems and Q&A capabilities that describe data, create captions and headlines, or call out interesting patterns and insights in data.
In summary, the goal of AI is to provide software that can reason on input and explain on output. AI will provide human-like interactions with software and offer decision support for specific tasks, but it’s not a replacement for humans – and won’t be… at least for now.
What Does AI Mean to ETMA (Where is AI Going?)
Jeff Bezos said, “…there’s no institution in the world that cannot be improved with machine learning… much of what we do with machine learning happens beneath the surface. Machine learning drives our algorithms… though less visible, much of the impact of machine learning will be… quietly but meaningfully improving core operations.” This is how it will impact ETMA members.
Gartner predicts that more than 40 percent of data science tasks will be automated by 2020. ‘’The result will be access to more data sources, including more complex data types; a broader and more sophisticated range of analytics capabilities; and the empowering of a large audience of analysts throughout the organization, with a simplified form of data science,” according to Gartner, Inc.*
ETMA identified more than eight use case opportunities with Razorthink for AI to be deployed by TEM, WEM and MMS firms. Members are starting to adopt these technologies.
*(Source: Gartner Press Release, Sydney Australia, January 16, 2017)
AI and Industries
Technological innovations have driven economic growth for more than 200 years. The most important innovations come from what economists call general-purpose technologies a category that includes the steam engine, electricity, the internal combustion engine, and the internet. Each one created waves of complementary innovations and opportunities. The internal combustion engine, for example, gave rise to cars, trucks, airplanes, chainsaws, and lawnmowers, shopping centers, big-box retailers, cross-docking warehouses, new supply chains, and suburbs.
The biggest advances in AI have come from two broad areas: perception and cognition. Speech recognition has improved dramatically. A study by the Stanford computer scientist James Landay and colleagues found that speech recognition is now about three times as fast, on average, as typing on a cell phone. Since the summer of 2016, the error rate, once 8.5%, has dropped to 4.9%.
Image recognition, too, has improved dramatically. Image recognition is even replacing ID cards at corporate headquarters. The error rate for recognizing images from a large database called ImageNet, with several million photographs of common, obscure, or weird images, fell from higher than 30% in 2010 to about 4% in 2016 for the best systems.
The second type of major improvement has been in cognition and problem-solving. Machines have already beaten the finest (human) players of poker and Go — achievements that experts had predicted would take at least another decade. Google’s DeepMind team has used ML systems to improve the cooling efficiency at data centers by more than 15% after they were optimized by human experts. Dozens of companies are using Machine Learning to make credit decisions. Amazon employs ML to optimize inventory and improve product recommendations to customers.
Artificial intelligence will be bigger and more disruptive to more industries than the internet and mobile technologies.