What is Artificial Intelligence (AI) and Machine Learning (ML)?
Artificial Intelligence (AI) is a broad category that includes cutting-edge concepts such as deep learning. In general, AI is all about bringing aspects of intelligence to machines and having them perform tasks which can be natural and easy to humans, but extremely complicated to program. Moreover, an AI agent can execute such tasks autonomously and efficiently.
AI-driven solutions can be classified into two groups: general and narrow. General AI (also called strong AI) is what data scientists aim to develop in the future. It would be designed to solve broadly-defined problems intelligently thanks to sophisticated cognitive abilities and general experiential understanding of its environment. Today, that might sound like a science fiction scenario - but someday it’ll become a reality.
But AI is already changing the world as we know it - in the form of narrow AI, which focuses on performing specific tasks with incredible performance, often better than humans. For example, Pinterest uses a narrow AI solution for tagging images on the platform.
Machine learning is one of the techniques software engineers use to build AI solutions. Just as the name suggests, machine learning is all about helping machines to learn on their own with the help of datasets provided by software developers or data scientists.
The idea is that, during training, a model is shown large amounts of data to learn from, which allows it to discover non-trivial patterns. Afterwards, such models can make smart decisions and offer accurate predictions on previously unseen examples. To train machine learning algorithms, developers need to provide them with a lot of data. That’s how ML tools learn more about the processed information and extract rules automatically from a pile of data to generate insights or perform the most relevant actions.
What data can you use for building AI solutions and how to do it?
Developers who want to create an AI-powered solution need to train algorithms on a set of diverse data - for example, a collection of images, text, or specific information like financial transactions or products viewed by users.
You can buy prepackaged data, take advantage of public crowdsourcing initiatives (such as the Amazon Mechanical Turk) or - when dealing with potentially sensitive data - hire private crowds of data science specialists able to help you out in data collection, identification, and labeling.
The dataset used for training needs to include a sufficient number of both positive and negative examples to help algorithms learn from it. For instance, if we want our algorithm to identify ugly cranes on pictures, we need to show it pictures both with cranes and without them.
The developer or data scientist may experiment with different algorithms before deciding which one is the best fit for the training data. But that’s not everything. We also need to provide the developer with a test set, a dataset used to test the model developed on the basis of the training data for evaluation and improvement.
Real-world and business applications of AI
Businesses use AI solutions to automate tasks that were previously completed manually - for example, data digitalization, text summarization, language translation, or reporting. Companies also use AI to build intelligent conversational chatbots that can provide their customers with excellent service instantly, every hour of every day.
AI helps organizations to better respond to the current and future customer needs. For example, an AI-driven financial solution may help sales reps create personalized suggestions of the best products or packages for people on the basis of their previous choices and specific contextual data (for example, upcoming retirement).
Organizations also use AI to boost their cybersecurity systems by predicting potential threats and identifying abnormal behaviors based on the analysis of massive datasets. Finally, AI comes in handy for manufacturers looking to automate specific tasks with the help of robots able to optimize their operations thanks to machine learning.
What is the future of AI and ML?
According to Gartner, “Artificial Intelligence and Machine Learning have reached a critical tipping point and will increasingly augment and extend virtually every technology-enabled service, thing, or application.” They also predict that by 2020, AI will become one of the top five investment priorities for at least 30% of Chief Information Officers.
Consumers are now increasingly used to digital assistants, self-driving cars, robots working in factories, and smart cities. AI made its mark on most industry sectors and continues to spread to new industries.
Experts predict that Machine Learning will grow at an increasing rate. Some people believe in the inevitable rise of that technology offered as a cloud-based service - the so-called Machine Learning-as-a-Service (MLaaS).
Ultimately, Machine Learning will keep on helping our machines to make better sense of data; both its context and meaning. And these powerful and actionable insights will make AI-based solutions indispensable for data-driven decision making among executives and project managers of the future.
How Netguru can help you achieve business goals with AI-based solutions
At Netguru, we believe that organizations which dare to implement innovative technologies like AI will become leaders in their industries.
But implementing AI or ML solutions only makes sense if they serve specific business goals. That’s why we aim to help our clients make the most of techniques like ML and use our expertise to deliver solutions that bring tangible business value.
Our AI-powered solutions are deeply integrated into the business models of our clients, helping them to improve business processes, boost the productivity of their staff, and achieve critical business objectives faster.