AI in Lerpal practice
Natural Language Processing and Machine Learning fall under Artificial Intelligence as an umbrella term meaning machines simulating human intelligence. Natural Language Processing focuses on techniques where computers understand and translate the human language. Machine Learning applies algorithms to make machines automatically learn and improve from experience.
Artificial Intelligence Serving Art
Heated discussions about AI in marketing continue to dominate the tech landscape, but what about its impact on industries like art and healthcare? In this blog post, we are going over just a few examples from Lerpal’s own experience.
Even if you are skeptical about literature created by ChatGPT or pieces of art by Dall-e, there is still room to be optimistic about AI in Art in general.
Real-time valuation of art assets is a challenge we had to tackle throughout a long-term cooperation with one of our customers. We employed machine learning to simulate human expert decision-making to create and analyze a real-time database with art deals considering all available online art trends. Thanks to pattern recognition, data analysis, neural networks, decision trees, and ensemble modeling, the platform predicts the best moment in time to sell an artwork. The output, being an instantaneous and accurate estimate, is a true game-changer for the industry. To sell a piece of art, it is no longer necessary to spend plenty of time to qualify for an auction or tons of money to hire a professional valuer.
HealthTech Automation
Healthcare belongs to the most crucial, essential, and therefore responsible industries. Lerpal used Natural Language Processing to help clients create an AI-driven platform aimed at improving people’s lives through rethinking nutrition. Working closely with the founder and CEO of Just a Bite Better, we turned his vision into reality: text messages about users’ eating and drinking habits are being seamlessly transformed into personalized nutrition plans based on advanced mathematical models. With the adoption of Microsoft’s LUIS and Azure Bot, we have seamlessly delivered complex backend processes, bringing to life features envisioned by the client and loved by end users. Users have reported an average 30% improvement in overall well-being thanks to Just a Bite Better. They granted our application an impressive 4.9-star average rating across app stores.
Want to turn users’ insights in natural language into mathematical algorithms to offer brand-new services? Or have a plan to employ machine learning to make expensive and complicated processes simple and affordable?
Turning raw vision into a sustainably performing high-quality solution requires a reliable software development partner. Who knows, maybe Lerpal is the one for you?
Click “Get in touch” and let’s find out!
FAQ
The least effective qualities that we all want to avoid are poor communication skills, a lack of strategic thinking, and an inability to adapt to unforeseen challenges. Failure to manage stakeholder expectations and absence of transparency regarding project status can also contribute to their shortcomings. It’s like working with a project ghost – spooky and not at all helpful.
Artificial intelligence (AI) refers to the capability of a digital computer or computer-controlled robot to perform tasks typically associated with intelligent beings. The term is often used to describe efforts to develop systems that can replicate human intellectual processes, such as reasoning, discovering meaning, generalizing, and learning from past experiences.
Since the advent of digital computers in the 1940s, these machines have been programmed to perform highly complex tasks with impressive proficiency, such as solving mathematical theorems or playing chess. However, despite advancements in processing speed and memory capacity, no AI programs have yet achieved the full range of human flexibility, particularly in tasks that require extensive everyday knowledge.
That said, some AI programs have reached the performance levels of human experts in specific tasks. This form of AI, known as narrow AI, is utilized in various applications, including medical diagnosis, search engines, voice and handwriting recognition, and chatbots. While AI has not yet matched human intelligence across all domains, its capabilities in specialized areas continue to expand rapidly.
Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy.
According to UC Berkeley, the learning system of a machine learning algorithm can be broken down into three main parts:
- Decision Process:
- Machine learning algorithms are designed to make predictions or classifications based on input data, which can be either labeled (with known outcomes) or unlabeled. The algorithm analyzes this data to identify patterns and generate an estimate or prediction.
- Error Function:
- An error function is used to evaluate the accuracy of the model’s predictions. When there are known examples available, the error function compares the model’s predictions against these examples to determine how well the model is performing.
- Model Optimization Process:
- To improve accuracy, the model undergoes an optimization process. If the model’s predictions do not align well with the data points in the training set, the algorithm adjusts the weights associated with different features to reduce the error. This iterative process of evaluation and optimization continues until the model reaches a satisfactory level of accuracy.
These components work together to enable machine learning algorithms to learn from data, refine their predictions, and improve over time.
Natural Language Processing (NLP) is a machine learning technology that enables computers to interpret, manipulate, and understand human language. With the growing volumes of voice and text data generated through various communication channels—such as emails, text messages, social media feeds, videos, and audio—organizations increasingly rely on NLP software to manage and analyze this data.
NLP software automatically processes the data to identify the intent or sentiment behind messages, allowing businesses to respond to human communication in real time. This capability is crucial for improving customer interactions, streamlining operations, and gaining insights from unstructured data.
