Airbnb enjoys a nearly 4% lift in booking conversion thanks to Artificial Intelligence
What started as a small research project resulted in the development of a machine learning model that learns our hosts’ preferences for accommodation requests based on their past behavior. The model showed about a 3.75% increase in booking conversion, resulting in many more matches on Airbnb.
Artificial Intelligence can be used across practically sectors and industriesArtificial Intelligence can support your business in many ways.
Starting an Artificial Intelligence project is not easy. Choose the right solutionThere is a variety of Artificial Intelligence solutions tailored for different business needs. Let’s work together
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.
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.
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.
World-class specialists at your disposal
Personalized Shopping with Countr
Countr is a personalized shopping app that enables its users to shop with their friends, receive trusted recommendations, showcase their style, and earn money for their taste – all in one place. When it comes to ML, we delivered the recommendation and feed-generation functionalities and improved the user search experience.
Speech-to-Text transcription with CocoonWeaver
We have designed an intuitive UX and developed a neural network that, together with Siri, enables the app to perform speech-to-text transcription and produce notes with correct grammar and punctuation.
Audio recognition with Baby Guard
To achieve a high performance, we used custom audio processing algorithms and neural networks to handle the classification of the signal. The system can detect a baby’s cry rapidly and accurately. Our designers handled the UX to make the app easy and intuitive to use.
Our partners about the cooperation with Netguru
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Frequent questions asked by our clients.Based on our experience and questions we receive from our clients, we wrote down a list of Frequently Asked Questions. We hope it will help you better understand the issues related to Machine Learning. Let’s work together
Enterprise software development projects are very difficult to estimate. Adding an Artificial Intelligence (AI) modules make them much more challenging. That is why you should keep in mind that you are asking engineers to write a program that will generate a program, which will learn to do something useful for your business. It is complicated. It's difficult to answer this question in few sentences that is why we've answered it on our blog.
Netguru offers a variety of services, from data collection strategy to building a scalable machine learning infrastructure.
AI Design Sprint - rapidly validate your Machine Learning project
ML Processes Audit - verify your Machine Learning delivery processes
Data Quality Assessment - plan your data collection strategy
ML-Ops Transformation - build a scalable Machine Learning infrastructure
Data-Ops Transformation - build a scalable Data infrastructure
Artificial Intelligence applications can bring you more clients, increase sales and reduce business costs. However, if not used properly, they may lead to customer outflow, money loss and reputation damage.
Data is the key to success (or lack thereof) of AI applications. Short and simple: in traditional software development, humans create computer systems, and machines simply follow these pre-programmed rules. Thus, the crucial part of the application is the algorithm inside.
There are hundreds of business applications of artificial intelligence. In general, it solves several types of problems. The main ones are:
Classification: Is this credit card transaction fraudulent or not? Is this email spam or not? Machine learning is a great tool when you need to divide objects (for example clients or products) into two or more pre-defined groups.
Clustering: ML discovers patterns in chaos. It enables those who use it to find parallels between data points and divide objects into similar groups (clusters). What is important, there is no need to define the groups in advance.
Regression: It's like future prediction. On the basis of an input from a dataset (usually historical data plus other factors), ML estimates the most likely numeric value of a particular quantity. It could be anything, such as stock or real estate prices, consumer behaviour, or wear and tear on a piece of equipment.
Dimensionality reduction: In an ocean of information, ML can choose which data are the most significant and how they can be summarised. In practice, it is applied in such fields as photo processing and text analysis.
Although AI gives businesses numerous new options, there are situations when it's better to stick with traditional software methods.
When are you better off avoiding AI?
You don't have enough data: Artificial Intelligence is designed to work with huge amounts of data. Really huge. 100k records is a good start. If the training data set is too small, then the system's decisions will be biased.
Data are too noisy: "Noise" in AI is the irrelevant information in a dataset. If there is too much of it, the computer might memorise noise. This was the case of Tay.
You don't have much time (and money): AI is time- and resource-intensive. First, data scientists need to prepare a dataset (if they don't do it, see point no. 2). Then, the computer needs some time to learn. Then the IT team performs test and adjusts the algorithm. Then, the computer needs some time to learn, again. IT does some testing, and adjusts the algorithm. The computer goes back to learning... The cycle repeats over and over again. The more time is needed, the more you need to pay IT specialists.
You have a simple problem to solve.
To sum up: Artificial Intelligence helps find patterns in the chaos of big datasets. It is worth considering when you have a complex task to solve, or if you’re dealing with a large volume of data and lots of variables. But this method has its limits. It's better not to choose it if you are limited by time, or the amount or quality of available data
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