At this point, pretty much everyone is familiar with artificial intelligence, or AI. Thanks to movies such as The Matrix, Terminator, and well, AI: Artificial Intelligence, most people are aware of the concept of intelligent machines. But although AI is a real and genuine technology, the potential and tangible applications of modern AI advancements are far from what Hollywood movies and novels by authors such as Philip K. Dick would have us believe.
In fact, real AI is mostly applied to business development and science projects where data-driven derivatives and goals are the primary focus of a given strategy. In simple terms, AI applications are primarily focused on gathering, analyzing, and sorting data, much like any other computer program. However, where AI is different is in its machine learning capabilities.
What is Machine Learning?
What is machine learning? Well, machine learning is the concept of an AI developing its own repeatable output based on the data analysis from repeated input. For example, Facebook’s machine learning algorithm, DeepText, analyses posts and displays the ones that it thinks you might find interesting based upon your previous post-browsing history.
DeepText isn’t the only machine learning tool that you are exposed to on a daily basis. Netflix’s video suggestions, email spam filters, and even certain video games are all examples of machine learning that you probably use on a daily basis and don’t even realize.
Over the past few years, the power of machine learning and AI has become a viable tool in business development because of the analytical and data processing capabilities of modern hardware and accompanying software tools.
Common AI and Machine Learning Tools
Large corporations such as Amazon, Microsoft, and Google apply their own machine learning tools to their own business and have developed consumer-level AI suites for use by other businesses that don’t require knowledge of complicated AI programming and can be used for specific data analysis and modeling.
Some of the most useful AI suites include:
- Google Cloud AI
- Amazon SageMaker
- Microsoft Azure AI
- Obviously AI
One of the most robust and technological machine learning tools available, Google AI powers the company’s own algorithms and provides multiple data-driven features while Amazon SageMaker comes as part of the extensive Web Services suite with an ability to perform custom ML datasets, and Azure AI from Microsoft is an impressive suite with many real-world applications as well as analysis.
Two AI tools that aren’t from huge corporations are MonkeyLearn and Obviously AI. MonkeyLearn is a very user-friendly text analysis MLAI and Obviously AI provides natural language analysis for forming predictions based on specific input.
Google Cloud AI
Launched in 2008, Google Cloud AI is mainly a compounding structure for integrating the useful features of cloud computing into a single, manageable platform with Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) options for secure cloud services. However, the system also provides feature-rich machine learning tools as standard that can be utilized in a variety of ways.
One of the most powerful AI and ML systems available, Google AI prides itself on being able to take under-researched problems and find solid, scientific solutions that advance the state-of-the-art in the field. Google also has a mission to make its AI system available to everyone and apply the technology to solve some of the world’s most pressing humanitarian and environmental issues.
The suite comes with many impressive AI applications that you can integrate directly into your deployment and productivity pipeline almost immediately:
- Contact Center AI; allows you to automate your call center interactions with customers.
- Documents AI: can read and extract data from your documents with algorithms that can look for fraud, data entry errors or receipt mistakes.
- Recommendations AI: can deliver personalized recommendations for each of your customers based on their previous interactions.
Part of Amazon Web Services, SageMaker provides multiple machine learning bindings for some of the most widely used and powerful programming languages available such as Python, Ruby, and Java which allows the creation of custom datasets and algorithms in addition to an impressive collection of preexisting ML systems that can also be incorporated into cloud computing.
One of the most impressive features of SageMaker is its support for other ML platforms with which it can integrate for even more productivity and increased workflow. Google Brain’s open-source platform TensorFlow is compatible with SageMaker as is Tableau. Both of these are focused on business intelligence and deep learning with traditional ML at their core.
SageMaker is one of the most powerful, scalable ML platforms available with many useful features:
- Clarify; reduces bias in ML that could help you find suitable employee candidates or customers.
- FeatureStore; allows you to select from multiple pre-existing feature groups that can be applied to your specific requirement model.
- Data Wrangler; this allows you to manipulate and convert data from one type to another, useful for such things as data modeling, calculation, and visualization.
Microsoft Azure AI
As the world’s leading software manufacturer, Microsoft is no stranger to complex data and advanced programming such as AI and ML. Also a cloud compounding service, Azure AI provides SaaS, PaaS, and IaaS capabilities with support for many of the best and most flexible programming languages available, along with their frameworks, with the suite’s emphasis being on software development by utilizing data sets, storage, and management features.
The business applications of Azure are impressive and the platform has been built from the ground up to accommodate business processes that accelerate development and rapid deployment in scalable, automated, and reproducible ML scenarios using specific data, models, and processes.
While most other ML platforms can be used for a broad range of applications, Azure AI is laser-focused on business development strategy:
- Video Analyzer; allows you to obtain real-time analytics from played and streamed videos.
- Azure Cognitive Search; add feature-rich data sets to your search functions such as autocomplete, geospatial filtering, and keyphrase extraction.
- Adaptive Machine Learning; user friendly interface with advanced features allows you to develop ML scenarios with either beginner or expert knowledge.
One of the best Software-as-a-Service machine learning tools around, MonkeyLearn provides a feature-rich set of machine learning capabilities at a low cost that can provide useful insights for any business, but is especially popular with small to medium-sized businesses as one of its best features is that no coding is required.
Using MonkeyLearn’s user-friendly interface, you can develop topic-specific models for ML datasets such as keyword extraction, text to tag conversion and text classification features such as sentiment analysis, topic labeling and intent detection; true machine learning that can boost the productivity of any data-dependent business.
Some of the most useful features of MonkeyLearn include:
- Text Extraction; analyzes data so you can pick out features, keywords, or entities such as “14” screen size”, “free delivery”, or “Disney”, respectively, for example.
- Text Classifiers; analyses data so you can classify text according to a topic, intent, or sentiment.
- Integration; MonkeyLearn can further increase your ML capabilities by integrating with other popular programs such as ZenDesk, Google and MS Office, and Rapidminer.
Another excellent machine learning application, Obviously AI uses natural language processing as a means of producing prediction models based upon specific input datasets. The platform requires no programming and operates via a user-friendly point and click interface in order to upload data and model a scenario for prediction with naturally written questions.
The MLAI then works to predict a specific outcome based upon the questions input and a predefined prediction column, then it can accurately model such things as revenue forecasts, lead conversions, and loan repayment probabilities in minutes, saving both time and money.
Basic on the surface, but extremely complex at the core, Obviously AI’s best features include:
- Complex Models; the app comes with built-in models that you can use right away for predictions.
- Ease of Use; the intuitive and user-friendly UI facilitates high-level machine learning with no expert knowledge of coding.
- Business-Boosting ROI; predictions with the app allow you to make informed decisions that can increase sales, detect fraud, or estimate paywalls and customer willingness to pay.
A Binary Decision?
It is pretty clear that machine learning and AI are driving the future of many industries, with large corporations at the wheel. Google, Amazon and Microsoft are empowering businesses of all sizes and across all sectors to advance their strategies with the inclusion of AI analytics, automation and prediction models.
The capabilities of AI and machine learning seem almost limitless and from a business perspective are able to extremely reduce the amount of man hours and, therefore, finances required to perform specific actions such as document analysis, categorizations and data extraction.
As AI and machine learning become more prevalent throughout all business sectors, one should ponder the questions; where does this leave the necessity of the human mind and what will be the human cost of such technological innovation?