Machine learning (ML) may have taken a back seat to Gen AI in the current furore around the technology, but the process is an important underpinning of many AI applications.
It has had a longer time to establish itself in many industries, either influencing their development or creating entirely different uses or products within them.
Now thanks to AI developments as a whole, the industry is developing beyond its functions we have become accustomed to and new applications are being made.
In recognition of this, AI Magazine takes a look at 10 of the top uses of ML currently in play.
10. Virtual personal assistants
Key business: Apple
These AI-powered digital helpers have been evolving since the early 2010s to manage tasks, answer queries, and control smart home devices
Virtual personal assistants have become an integral part of our daily lives, offering seamless access to information through text or voice interfaces. These AI-powered assistants analyse queries, gather relevant data from various sources, and provide personalised responses based on user history and preferences.
Employing sophisticated ML techniques such as speech recognition, natural language processing, and text-to-speech conversion, virtual assistants continuously improve their understanding and interaction capabilities.
A key player in this field is Apple, with its virtual assistant Siri. According to Apple, Siri handles over 25 billion requests monthly across 36 countries, demonstrating the widespread adoption and utility of this technology in enhancing user productivity and convenience.
9. Online fraud recognition
Key business: Visa
The advanced AI systems have been rapidly analysing transactions since the late 2000s
In the realm of financial security, ML plays a crucial role in detecting and preventing online fraud. Every transaction triggers a complex analysis of the customer's profile, searching for anomalies or suspicious patterns that might indicate fraudulent activity.
This real-time scrutiny allows financial institutions to protect their customers and maintain the integrity of digital transactions. The sophistication of these ML models enables them to adapt to new fraud techniques, constantly evolving to stay ahead of cybercriminals.
Visa, a global leader in digital payments, utilises advanced ML algorithms in its fraud detection systems. According to Visa, their AI-powered fraud prevention technology has helped financial institutions prevent an estimated US$25bn in annual fraud.
8. Stock market and day trading
Key business: Bloomberg
ML algorithms, that have been revolutionising financial markets since the 1980s, now process terabytes of data in milliseconds for trading decisions
The application of ML in stock market analysis and day trading has revolutionised investment strategies. ML algorithms excel at processing vast amounts of financial data, identifying patterns, and making predictions that inform crucial investment decisions.
These systems can automate portfolio management, optimise buy and sell timings, and even execute trades based on predefined criteria. The ability to rapidly analyse market trends and react to real-time data gives ML-powered trading systems a significant edge in the fast-paced world of finance.
Bloomberg, a major player in financial software and media, offers ML-powered analytics tools to traders and investors. Their ML models processes 2.8 million securities,10 times per day, providing invaluable insights for investment decisions.
7. Catching malware
Key business: CrowdStrike
ML has helped the cybersecurity field evolve from signature-based to behaviour-based detection
The process of using machine learning (ML) to detect malware consists of two basic stages. First, it involves analysing suspicious activities within a given environment to generate a comprehensive set of features.
Second, the system is trained using machine and deep learning techniques on these generated features to detect future cyberattacks effectively. This approach allows for more dynamic and adaptive malware detection compared to traditional signature-based methods.
Cybersecurity company CrowdStrike employs ML in its malware detection systems. According to CrowdStrike, their Falcon platform leverages ML to analyse over 30 billion events daily, enabling them to detect and prevent threats in real-time.
6. Catching email spam
Key business: Microsoft
This technology, which has been progressing since the 1990s, now using complex algorithms to block billions of unwanted messages daily
One of the most ubiquitous applications of ML is in email spam detection. Email service providers employ sophisticated ML algorithms to classify incoming messages, effectively filtering out unwanted spam.
These systems continuously learn from user behaviour and feedback, improving their accuracy over time. The ability to adapt to new spam tactics ensures that users' inboxes remain protected from evolving threats.
Microsoft, with its Outlook email service, utilises advanced ML algorithms for spam detection. Microsoft reports that their AI-powered security systems processes more than 400 billion emails each month and blocks 10 million spam.
5. Self-driving cars
Key business: Tesla
Autonomous vehicles represent the most ambitious application of ML
The development of self-driving cars represents one of the most ambitious applications of ML. These vehicles rely heavily on unsupervised learning algorithms to collect and process information from cameras and sensors, enabling them to understand their surroundings and make real-time decisions.
The ML systems must interpret complex environments, predict the behaviour of other road users, and navigate safely under various conditions.
Tesla, a pioneer in electric and autonomous vehicles, is at the forefront of this technology. According to Tesla, their vehicles have driven over 47 billion miles in Autopilot mode, continuously improving their ML models with real-world data.
4. E-commerce product recommendations
Key business: Amazon
These AI-driven personalisation engines now drive significant portions of e-commerce revenue.
E-commerce platforms leverage ML algorithms to provide personalised product recommendations, significantly enhancing the shopping experience. These systems track customer behaviour, including past purchases, browsing habits, and cart history, to generate tailored suggestions.
The sophisticated ML models can identify patterns and preferences that may not be immediately apparent, leading to more accurate and relevant recommendations. Amazon, the e-commerce giant, is renowned for its product recommendation system.
Amazon reports that 35% of its sales come from personalised recommendations, highlighting the effectiveness of ML in driving e-commerce revenue.
3. Predict traffic patterns
Key business: Google
The models, analysing traffic patterns since the early 2000s, now provide real-time route optimisation in navigation apps
ML has transformed traffic prediction and management, offering more accurate and dynamic forecasts of traffic patterns. By analysing vast amounts of real-time and historical data, ML models can predict upcoming traffic conditions and identify the fastest routes for travellers.This application not only improves individual journey times but also contributes to overall traffic management and urban planning.
Google Maps, a widely used navigation application, employs ML algorithms for traffic prediction.
Google claims that their ML models can predict traffic conditions with over 97% accuracy, helping millions of users navigate more efficiently.
2. Speech recognition
Key business: Nuance Communications
Speech recognition technology, powered by ML, has become increasingly prevalent in our daily lives. ML software can accurately measure and interpret spoken words, converting speech signals into digital data for further processing.
This technology forms the backbone of various applications, from virtual assistants to transcription services and voice-controlled devices. The continuous improvement in accuracy and language understanding has made speech recognition an indispensable tool in human-computer interaction.
Nuance Communications, a leader in speech recognition technology, provides ML-powered solutions used in various industries. According to Nuance, their speech recognition systems process over 14 billion customer interactions annually across 75 languages and dialects.
1. Image recognition
Key business: NVIDIA
At the forefront of ML applications is image recognition, a technique that enables computers to identify and categorise objects or features within digital images. This technology has far-reaching implications across various sectors, from security and healthcare to retail and entertainment.
Image recognition systems can perform tasks such as facial recognition, object detection, and pattern identification with remarkable accuracy. The ability to process and understand visual data at scale has opened up new possibilities in fields like autonomous vehicles, medical imaging, and augmented reality.
NVIDIA, a leader in AI and graphics processing, provides cutting-edge image recognition solutions. NVIDIA reports that their ML-powered image recognition systems, such as NVIDIA DRIVE PX Pegasus, can process over 320 trillion operations per second, enabling real-time analysis of complex visual data.
******
Make sure you check out the latest edition of AI Magazine and also sign up to our global conference series - Tech & AI LIVE 2024
******
AI Magazine is a BizClik brand
