Introduction to AI for Enterprises
Let's take a look at an introduction to Artificial intelligence for enterprises and also explore most used AI techniques in enterprises.
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Today, enterprises are faced with the task of radically increasing operational efficiency as customer demands rise. This requires them to be agile in solving business problems and remain competitive in the market. Machines can solve many problems that humans cannot. They also have the ability to outperform humans at various tasks. Artificial Intelligence powers machines by providing a "brain." Today, AI plays a role in almost all Enterprise verticals including Healthcare, Insurance, Retail, Manufacturing, and Consumer Applications. Today’s business applications are transformed into intelligent and smart applications that have the potential to optimize selling prices, change buying behavior and to provide personalized recommendations based on real-time data. This Intelligent application stack will gain rapid adoption in industries as IT departments shift from the system-of-record apps to system-of-intelligence applications. The future of software lies in the development of smart applications and will keep on accelerating as people start to recognize the benefits of smart applications. In the future, Artificial Intelligence will be widespread in enterprise applications, which will provide future predictions and prior insights across an Organization's business processes.
So, what is Artificial Intelligence? Let's forget enterprise for a moment and try to understand what Artificial Intelligence is. When we speak about the term Artificial Intelligence (AI), it implies that a machine that can reason, think, plan, and communicate. Some of the characteristics of an AI system are:
The ability to solve problems through reasoning
The capacity to independently draw information about the environment
The ability to experience various events and respond to situations autonomously i.e. when predefined rules do not exist
The ability to set and plan sequences of actions and achieve goals
The capacity to communicate effectively by a written and spoken language.
The ability to perceive things about the world from visual images, sounds, and other sensory inputs.
AI for Enterprises
As we shall see, Artificial Intelligence has always been an active branch of Machine Learning. But AI has often relegated to the boundaries of research and academic communities. It is now again in the limelight, which has been possible due to the recent development in the field of Machine Learning, Big Data processing, and innovation is driven by both in-house research and Open Source communities.
Enterprises are viewing Artificial Intelligence as a possible game changer and a potential solution to many long-lasting problems they have had in their business. Enterprises must either build or buy their intelligent platforms. Of course, this requires them to make some initial investments. They need to have sufficient computation power, acquire high-performance systems with GPU's, or obtain clusters in the cloud. Given these significant investments, the timeframe for enterprise AI adoption is often quite long. However, many enterprises are already undertaking AI deployments, as they see it as an investment in the future. For example, the retail industry is already considering scenarios where customers can upload pictures of the product they want on the retailer's websites or mobile application. The intelligent application would then search for the product and offer recommendations that save the customer from the inconvenience of searching. This has been made possible by AI systems powered by the availability of Big Data, greater computation powers and new algorithms like Deep Learning.
Most Used AI Techniques in Enterprises
In this section, we will see some of the AI techniques that are currently used for Enterprise solutions. We will discuss each technique in our upcoming articles. Apart from the techniques described in this section, you might find many more theories, methods, and algorithms for Artificial Intelligence solutions. However, most of them are not yet ready to be a part of the Enterprise AI framework and still lacks enough reason to be used in an Organisation's production environment.
Artificial Intelligence is not just Machine Learning. However, Machine Learning is an important part of artificial intelligence, and the recent success of artificial intelligence is connected much to the growth of Machine Learning. We can define Machine Learning as algorithms that learn and gain experience from the data for identifying patterns to predict the future or to obtain some useful information out of it. So if we want to predict the future sales of our organization, we can run the past historical sales data through a Machine Learning algorithm; and if it has successfully learned the pattern of sales, it will do better at predicting future sales. We will find out more about Machine Learning in the next chapter.
Deep Learning is one of the primary drivers to pull Artificial Enterprise out from its long winter by enhancing the idea of Neural networks by incorporating many layers. Deep Learning refers to artificial neural networks that are composed of many layers. The ‘Deep’ refers to multiple layers. The ability of Deep Learning methods to learn complex nonlinear relations makes it stand out from the traditional Machine Learning techniques. Neural networks are the precursors to deep learning. What makes deep learning different is the use of a high number of hidden layers. The presence of multiple layers allows the network to learn more abstract features. Thus, the higher layers of the network can find out more abstract features building on the inputs from the lower layers. A Deep Learning network can be seen as a feature extraction layer with a classification/regression layer on top. The power of most deep learning algorithms are not their classification skills, but rather in the feature extraction techniques. Feature extraction is automatic (without human intervention) and multi-layered.
In a nutshell, the deep learning network is trained by exposing it to a large number of labeled examples. Errors are detected and the weights of the connections between the neurons are adjusted to improve results. The optimization process is repeated to create a tuned network. Once deployed, unlabeled datasets can be assessed based on the tuned network.
Deep Learning suits problems where the target function is complex, datasets are large and with a variety of cases.
In this method, a new task can be learned by transferring the knowledge from a related task that has already been learned. This has immense application for reusing models across domains, in areas where the data is sparse. As an example, using transfer learning, one can develop a sentiment analysis model on some products where abundant reviews are available and use this knowledge to develop the same type of model for some other products with sparse reviews.
In this learning method, a system automatically tries to understand the situation, learn from the interactions, and choose the optimal path for itself to attend its objective. This is based on a reward system where the learner is not told what action to take but rewarded when it takes the correct decision. This method is same as how a student learns — rewarded when he excels in the exam and punished when he fails. This is a niche area for data scientists and AI professionals to explore and contribute.
Natural Language Generation (NLG)
NLG turns raw data into a language that any audience with no knowledge of the raw data can understand. The simplest level of NLG is to turn a few data points to sentences. This is a niche area, and Data Scientists should learn the approach and techniques to embed NLG in analytics systems. Currently used in customer service, report generation, and summarizing business intelligence insights.
Natural Language Processing (NLP)
NLP is the ability to comprehend human language. This may be written text, speech, or video. The natural language doesn’t have any fixed structure. This makes it difficult to store and process data. NLP is a current hot topic, and we can find several solutions revolving in this space. However, there is still a lot more to achieve and it is a very active area of research. Currently used in fraud detection and security, a wide range of automated assistants, and applications for mining unstructured data.
Automated Machine Learning
Automated Machine Learning is an area or a framework where statistical routines and Machine Learning are automated. The system executes the best algorithm based on the provided data set. It hides the intricacies and mathematical complexity of algorithms from the user making it available to masses. The user needs to provide an automated system with data. It understands the data, creates different mathematical models and returns the result based on a model that best explains the data. It is a complex science as it requires the system to learn the input data patterns, find the best fit values and self-optimize its parameters using several statistical and Machine Learning algorithms. This requires the generalization of various algorithm constraints and enormous computing power.
Automated Machine Learning is gradually maturing by leveraging cloud-based servers to manage the requirement for high computational power. Organizations creating data products are progressively including features such as meta-learning, a process of automatically selecting a suitable Machine Learning algorithm based on the metadata of the data set. Also, there has been a breakthrough of bringing Neural Networks and Deep Learning to mainstream for automated data science tasks. Some of the niche Artifical Intelligence startups like H2O.ai are the forerunners of creating in-memory optimized deep learning and Machine Learning algorithms, generalized model building process by introducing several built-in functionalities and providing many model tuning options such as hyperparameter tuning which support to have greater control over the algorithms.
Last but not least, feature engineering is not a direct descendant for AI techniques or Machine Learning Algorithms. However, it is one of the important techniques that plays a vital role in shaping up the AI solutions. Feature engineering is a technique to convert raw data into attributes/predictors that contribute to improving the accuracy of a Machine Learning project. Feature engineering automation is still at a nascent stage and an active area of research. It involves finding connections between variables, data transformations such as imputing null values and algorithm specific transformations to name a few. Also, some deep learning algorithms like Convoluted Neural Networks(CNN) has layers to performs automated feature engineering. Automated feature engineering is the defining characteristic of Deep Learning especially for unstructured data such as images. This matters because the alternative is engineering features by hand. This is slow, cumbersome and depends on the domain knowledge of the people/person performing the Engineering.
In the next piece, we will deep dive into the components that are required for building an AI-enabled Enterprise.
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