Now let's move on to machine learning which is a subset of artificial intelligence. It provides computers with the ability to learn without being explicitly programmed and utilize predictive analytics to forecast outcomes and also to assess the probability of future predictive events.
Machine learning has the ability to identify risk and to identify opportunities for businesses by using cognitive computing techniques, and it supports much greater efficiency. It can understand and can respond to human sentiments and emotions as well. Machine learning has gotten beyond the capabilities of predictive analytics and beyond the capabilities of big data analytics.
It also surpasses in some ways human capabilities by thinking independently and making its own evaluations and its own conclusions. If we look to the machine learning development, we see that it's accelerating very quickly, and we will discuss now the cause of this rapid advancement.
One such cause is the recent explosion of big data. Data is everywhere, and it is expanding from a number of sources. You can think about the sources for text, for images, for digitized documents and for internet devices. Everything is connected right now.
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Easier to Train
These sources make it easier for machines to train and to learn and as a result this enables more accurate predictions, more insights, more recommendations, and it drives business innovation and experimentation. Training big models and sophisticated models requires a lot of input data to determine weights for a lot of variables.
It's the processing power and training big models within meaningful amount of time. It's also step wise improvement in the underlying machine learning and deep learning algorithms which enable us to train much, much deeper models essentially from scratch and standing on the shoulders of giants and then looking back makes you wonder why we haven't been able to train these deeper models already in the late 90s or early 2000s.
It's because of a lot of experimentation was required to figure out the exact algorithms, the initialization, the weight functions to make training deeper models from scratch actually viable. Today we are at a juncture point with these great trends. Data processing power and improved algorithms have come together creating a virtuous cycle creating new potential from machine learning and artificial intelligence.
This brings us to the third topic of discussion today which will focus on the business benefits of machine learning. Machine learning technology has enabled businesses to work with unstructured data like text and images and videos. On the other hand we have natural language recognition. It's also abbreviated as NLR, and this is the ability of a computer to understand the human language.
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Companies are using this natural language recognition as well to strengthen their analytic foundation and to better understand the needs of their clients to achieve a more expansive operational environment. Also, if you look for images, machine learning is used to determine if an image has certain objects. An example could be facial recognition, and we know that for example, Facebook is using this.
It has a nine layer algorithm. It is a deep learning algorithm where they find the face and image. They analyze it, and they compare it against all the known faces, and then they make a prediction based upon these factors, and they can do it this way with an accuracy of 97.5% which is remarkable I think.
Machine learning can also recognize objects in videos, and this enables developers to build applications that can automatically extract information from a video. This is also being further developed into an API. There are numerous benefits for businesses who utilize machine learning, and it definitely enables faster decision making. It can automate processes which facilitate better outcomes.
Example of Machine Learning Applied to Language Translation Technology
Machine translation (MT) has revolutionized how we bridge the communication gap between languages. Gone are the days of relying solely on dictionaries and phrasebooks. Today, machine learning (ML) sits at the heart of MT, enabling more accurate, nuanced, and efficient translations. Let's delve into how ML fuels this remarkable technology.
The Power of Statistical Analysis: Traditional MT
Early MT systems relied on rule-based approaches. Linguists painstakingly defined grammar rules and created dictionaries to map words between languages. However, these methods struggled with the complexities of natural languages, often producing awkward and grammatically incorrect translations.
The introduction of statistical machine translation (SMT) marked a significant leap forward. SMT utilizes ML algorithms that analyze vast amounts of existing human translations. These texts serve as a training ground, allowing the algorithms to identify statistical patterns in how words and phrases correspond across languages. By recognizing these patterns, the system can then predict the most likely translation for a new piece of text.
Neural Networks: Mimicking the Human Brain
While SMT offered a significant improvement, it still faced limitations in capturing the subtleties of language, such as context and sarcasm. This is where neural machine translation (NMT) enters the scene. NMT leverages artificial neural networks, a type of ML inspired by the structure and function of the human brain.
These neural networks consist of interconnected layers of artificial neurons that process information. During training, NMT systems are exposed to massive datasets of parallel text – sentences in both the source and target languages. The network learns to analyze the relationships between words, phrases, and sentence structures across the languages. Over time, it develops the ability to not just translate individual words, but to grasp the broader meaning and context of a sentence.
Key Advantages of Machine Learning in Translation
The integration of ML into MT offers several advantages:
- Accuracy and Fluency: NMT produces more natural-sounding translations compared to SMT. By considering context and sentence structure, NMT can capture the intended meaning more effectively, leading to translations that are both accurate and fluent.
- Adaptability: ML algorithms can continuously learn and improve based on new data. As NMT systems are exposed to more translated content, they become adept at handling new phrases, slang, and evolving language patterns.
- Multilingual Capability: Unlike traditional rule-based systems, NMT can be trained on multiple language pairs simultaneously. This opens doors to translating between a wider range of languages, breaking down communication barriers across a global scale.
- Speed and Efficiency: ML algorithms can process vast amounts of text in a fraction of the time it takes a human translator. This allows for near-instantaneous translations, making communication and information sharing more efficient.
Beyond the Basics: Fine-tuning Machine Translation
While NMT has come a long way, it's important to remember that it's not a perfect replacement for human translators. Here's how human expertise continues to play a crucial role:
- Domain-Specific Training: NMT systems can be fine-tuned for specific domains, such as legal documents or medical reports. Human translators with domain expertise can identify and address industry-specific terminology and nuances, ensuring the translated content is accurate and relevant.
- Cultural Awareness: Language goes beyond words; it reflects cultural context. Human translators can ensure that the translated message retains the intended cultural meaning and avoids potential faux pas.
- Quality Assurance: Even the most advanced NMT systems can make mistakes. Human post-editing remains essential to ensure the final translation is accurate, clear, and conveys the desired tone.
The Future of Machine Translation with Machine Learning
The future of MT is bright, with ongoing advancements in ML pushing the boundaries of what's possible. Here are some exciting possibilities:
- Real-time Translation: Imagine seamless, natural-sounding conversations across languages. Continued development in ML could see real-time translation tools become even more sophisticated and widely used.
- Speech Recognition and Translation Integration: The ability to translate spoken language in real-time would further break down communication barriers. Integration of speech recognition technology with NMT holds immense potential in this area.
- Universal Translation Systems: A future where language is no longer a barrier to communication might not be as far-fetched as it seems. Advancements in ML could pave the way for universal translation systems capable of handling a vast array of languages with exceptional accuracy.