What AI to Choose for Your Business? Explanation from CHI Engineers
Visit our blog to find more articles covering AI, mobile app development, IoT, and other technologies used for achieving ambitious business goals.
AI is becoming a mass trend in business year by year. 2 out of 3 companies already employ intelligent technologies or explore the opportunities they bring. But AI is not a single tech; it is a family of several branches that deal with specific tasks and applications. So which one fits your business goals?
If you are lost, we feel you. It is challenging to keep up with every opportunity intelligent technologies may offer. We gathered AI technologies and tasks they can solve in one practical cheatsheet to give you some hints and ideas.
AI Expertise in Action: Tailoring Technologies to Business Needs
Every AI domain can do a lot for business. As technologies develop rapidly, intelligent innovations can contribute to any business operation and add remarkable value. Let us show what we mean!
Natural Language Processing (NLP)
NLP fills the gap between people speaking their languages and computers interpreting the code. It helps machines understand, manipulate, and generate human language.
The tech can do the following for your business aspirations:
- Enable personalized chatbots in sales, marketing, and client service to understand natural speech and respond to queries;
- Translate text for easy content localization;
- Group information by keywords, titles, topics, and queries; also;
- Detect spam by recognizing “triggering” words in messages that might signal about unwanted content;
- Classify emotions: NLP makes machines catch the tone of messages in product reviews or social media comments;
- Generate text content for automated email communication, product descriptions on online marketplaces, and social media content;
- Provide content recommendations based on the user’s previous requests in the media & entertainment industry;
- Strengthen the functionality of search engines by understanding the user’s intent and context behind the query;
- Edit and improve texts to make your business communication, social media, and client conversations look more professional;
- Help your employees access the required corporate document by simply entering their query to the system;
- Summarize texts: The technology can save time by condensing user reviews and shortening important information minutes before meetings and events.
Computer Vision
Computer vision makes machines ‘see’ and ‘understand’ what is in front of them.
The tech does a lot in business:
- Detects, recognizes, and tracks objects: With computer vision, machines can find, name, and follow items in images and videos. It’s helpful for security systems, sports analysis, geomarketing, and logistics optimization;
- Recognizes faces for better personalization in retail, improved security in smart home systems, and easy access to personal devices and apps;
- Adds to augmented reality experiences in virtual fitting rooms and experiments with makeup and styles;
- Supports visual search to help customers quickly find an item (clothes or decor) using a similar picture instead of word description;
- Infuses robots, machinery, and self-driving cars with AI-powered cameras and lidars to navigate the environment, plan paths, detect obstacles, road markings, and street signs;
- Detects anomalies in healthcare to find tumors on medical scans, in agriculture to identify plant diseases or parasites as timely as possible, and in manufacturing to inspect products and equipment for defects;
- Capture and process data from text documents to, for example, check test answers or the driver’s ID, provide real-time translations, recognize street signs, process loans and invoices, etc. All these use cases help automate manual labor and optimize your workflow with that;
- Creates and edits visuals, for example, logos, product mockups, illustrations, and promotion materials;
- Builds 3D models for better product visualization.
Machine Learning
Machine learning (ML) enables computers to make decisions without direct instructions. ML algorithms can process large data sets, learn from them, divide information into groups, find patterns, and generate predictions. The technology supports strategic moves and daily operations of many departments, like sales, marketing, finance, and human resources.
You need machine learning expertise if you think about the following:
- Recommendations: ML algorithms are good at making suggestions based on data. With them, Netflix shows its viewers more exciting series, Amazon offers relevant goods, and social media feeds users with more engaging content;
- Predictive analysis: Machine learning models are gurus in analyzing data and foreseeing events before they happen. You can use them to:
- predict customer churn behavior;
- build a pricing strategy;
- forecast demand fluctuations;
- count credit scoring;
- plan your production schedule with equipment maintenance in mind;
- Segmentation: Algorithms can divide your clients, markets, and products by specific criteria (demography, geography, usage patterns, etc.) to help you target your efforts more efficiently;
- Data tagging: ML tools can categorize your corporate data based on its sensitivity and business value. For example, organizations can provide reliable security measures by continuously labeling datasets with tags like “strictly confidential”, “public”, or “for the X department”;
- Finding anomalies: You can use ML solutions to find unusual patterns in your data. For example, anomaly detection is commonly used in equipment monitoring, identifying network intrusion, health tracking, financial fraud detection, customer behavior analysis, and quality control.
Audio ML
Audio machine learning is an AI subfield that detects, processes and generates sounds. In business, it can do many things, for example:
- Recognize a person by voice as a part of the user authentication process in many apps and services;
- Monitor health: ML-powered health apps can detect and analyze sounds of snoring or coughing;
- Analyze sentiments in call center records to understand customer emotions, gender, language, or keywords pointing at a certain issue;
- Enable personal recommendations for music and podcast platforms;
- Change voice characteristics like tone, speed, pitch, or accent without changing what was said. The technology is used in media, entertainment, and assistive technologies to personalize customer interactions, localize audio content for a new market, and help people with disabilities;
- Find anomalies and predict maintenance: In production, audio machine learning can find existing or future equipment failures by detecting slight changes in production noises a human ear cannot catch;
- Convert speech to text and vice versa: Voice assistants, dictation tools, messengers, and automated transcription services transform spoken words into written pieces. Reading assistants, tools for the visually impaired, and multimedia content voiceovers do the opposite and convert text to speech.
Signal Processing
Signal processing is a technology that monitors, analyzes, and manipulates audio, visual, electromagnetic, electrical, and other types of signals. All the signals bring information the algorithms use for different purposes. What purpose could it be? Let’s take a closer look at the most popular use cases:
- Predictive maintenance & quality monitoring: Signal processing analyzes data from sensors and cameras to predict equipment failure and find product defects;
- Communications: The tech adds to the quality of signals in phone calls and video conferencing by strengthening good parts of signals and suppressing noises. It also improves data transmission speed by reducing file sizes without compromising quality;
- Content production: Signal processing improves audio & video record quality as well as sharpens and brightens colors on photos;
- Patient care & medical diagnostics: The tech improves the quality of medical images and enables monitoring devices to react to dangerous changes in patient conditions.
Attitude and Heading Reference System (AHRS) and Inertial Positioning
AHRS and inertial positioning will help when you need to find your way in an environment where satellites or visual references are unavailable. The technologies can track people and assets in buildings, mines, and undergrounds. They also can navigate airplanes, ships, and drones to determine position without satellite signals.
As for business purposes, these technologies can help you with:
- Smart homes in order to automate lighting, heating, and other services based on dweller’s location;
- Patient care by detecting falls and sending alerts when patients enter or leave specific areas;
- Client and employee monitoring to track visitor activities inside offices and detect suspicious behavior patterns (for example, in banks);
- Logistics operations by analyzing routes and machinery performance to optimize the supply management chain.
Unsupervised and Reinforcement Learning
AI algorithms need labeled data with special tags that help machine models understand it (imagine a photo of a labrador labeled ‘dog’). The majority of business data is unlabeled, and sometimes it’s impossible to accumulate and label data in advance. In such cases, unsupervised and reinforcement learning algorithms come into play. They can learn by themselves from any data.
Unsupervised learning models are good at finding hidden patterns in data, and reinforcement learning algorithms learn by receiving feedback about what is right and wrong. These instruments are also very useful for making data-driven decisions in uncertain or changing conditions. Now, how exactly can you use these features in a business realm? Here are a few ideas:
- Customer segmentation based on certain criteria for personalized marketing activities and product recommendations;
- Adjusting product prices according to fluctuations of demand and prices on the market;
- Supply chain management: Models help efficiently allocate resources and plan logistics to meet the demand at minimal cost.
In our original article, you’ll find more details about real-market AI solutions that are changing industries at this very moment. Click to read in full.
