Artificial Intelligence Explained: Chat GPT Alternative AI Tools & Other AI Applications
Artificial Intelligence (AI) has become a buzzword in the technology industry, and with genuine firm reasons. AI, no doubt has the potential to transform the way we live and work, and its applications are wide-ranging and diverse.
From remarketing to daily tasks like creating a shopping list, and complex mathematical and algorithmic calculations, AI is taking over.
In recent years, natural language processing (NLP) — a branch of artificial intelligence that helps computers understand, interpret and manipulate human language — and artificial intelligence (AI) have advanced rapidly, leading to the development of various tools, chatbots, and virtual assistant apps that can interact with users in (a natural manner) natural language.
There are several subfields or categories within AI besides Chatbots and NLP, including machine learning, deep learning, computer vision, robotics, and cognitive computing, as we are going to discuss today.
AI is used in a wide range of applications, such as autonomous vehicles, personal assistants, chatbots, fraud detection, and medical diagnosis, among other industries and sectors.
You might already know one of the best AI tools for marketers — Chat GPT, a large language model trained by OpenAI — interestingly an Elon Musk-founded Company.
While Chat GPT is an impressive AI-powered chatbot, there are several other apps and platforms that use similar technologies to offer a range of functionalities.
In this post, we’ll explore the endless possibilities of AI and the Impact it has already done within various sectors. We will talk about various Applications and AI platforms that can save you a huge amount of time by performing automating some of your daily tasks and doing for you the tedious ones.
“Nearly 69% of college graduates believe AI could take their job or make it irrelevant in a few years.” Tidio
Let’s start by defining the technology behind all this, AI.
What is Artificial Intelligence?
Artificial Intelligence (AI) is a branch of computer science that focuses on creating intelligent machines that can perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and natural language processing.
AI involves developing algorithms, models, and systems that can learn from data and make predictions or decisions based on that learning. These systems may be designed to work in specific domains such as finance, healthcare, or transportation, or to be more general-purpose in nature.
Benefits of Artificial Intelligence
Problem-solving and decision-making — AI systems can be trained using large amounts of data, allowing them to recognize patterns and make predictions or (assist in making) decisions based on that learning.
Virtual Assistants — you might be using the AI-powered search Engine Bing and their Chat (web co-pilot) that helps you browse faster, or Siri and Alexa, chatbots for customer service, and language translation, among other Virtual assistant AI applications.
AI in Uganda (Artificial Intelligence possibilities for Uganda)
The AI landscape in Uganda is still developing, there are some websites and AI companies in Uganda providing (AI services) a place to find information about AI such as national AI policies, blog posts, trends, data visualizations, live news, and publications.
It’s no doubt that AI has the potential to revolutionize various industries and sectors in Uganda, through a wide range of projects providing opportunities for growth and development.
For example, AI in Uganda is used in the field of agriculture by Makerere AI Lab and Sunbird AI among other AI companies in Uganda, specifically in machine learning for the prediction of deforestation, for Localized and Targeted Agricultural Advisory to Smallholder Farmers in Uganda. AI can be used to analyze weather patterns and soil conditions to predict crop yields, allowing farmers to make more informed decisions about planting and harvesting.
Isbat University in Kampala offers a Bachelor of Science in Artificial Intelligence & Machine Learning (BSC. AI & ML). A degree that prepares students to develop artificial intelligence applications, such as speech recognition, facial recognition, and language translation. Areas of study include data mining, advanced mathematics, engineering, and robotics.
AI Applications (How Artificial Intelligence Works)
AI (Artificial Intelligence) powered tools are software applications that leverage machine learning algorithms and other AI techniques to automate, optimize, and improve various processes in different industries. These tools help businesses and organizations to make faster, data-driven decisions, automate routine and repetitive tasks, and enhance customer experiences.
1. Machine Learning Introduction (What is Machine Learning?)
One of the most well-known AI applications is machine learning, Machine learning is a branch of artificial intelligence (AI) that focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
It involves training algorithms to recognize patterns in data and make predictions based on that learning. Machine learning is used in a wide range of applications, from recommendation engines on e-commerce websites to fraud detection in financial transactions.
Machine Learning Examples
A machine is a device that augments or replaces human or animal effort for the accomplishment of physical tasks. Machines can be simple, such as levers, screws, or pulleys, or complex, such as cars, aircraft, or robots (Tesla Bot). Machines can perform useful work, such as sewing, 3D printing, cooking or even writing, like ChatGPT which interacts in a conversational way.
Machine Learning Applications (Examples)
Financial Sector — Banks use AI to fight fraudulent activities.
Health & Fitness Industry — Smart health watches and wearables use AI to track your blood pressure and fitness progress.
eCommerce Industry — websites like Amazon and Jumia use AI in the backend to recommend products based on your favorites, search, and purchase history.
Social Media Marketing — Facebook uses AI, specifically Facial recognition to identify your friends’ faces and auto-tag them in your posts.
Travel Industry — the most common example is self-driving cars.
2. Natural Language Processing (NLP)
Another important application of AI is natural language processing (NLP), which involves teaching machines to understand and interpret human language. NLP is used in a wide range of applications, including virtual assistants like Siri and Alexa, chatbots for customer service, and language translation software. For instance, a chatbot can help a customer order food, book a hotel room, or even schedule a doctor’s appointment.
Natural Language Processing Examples
ChatGPT
In recent years, the development of natural language processing (NLP) and artificial intelligence (AI) has revolutionized the way humans interact with machines. One of the most impressive examples of this technology is Chat GPT, a large language model trained by OpenAI that uses AI to generate responses to user input. Although Chat GPT is an impressive AI-powered chatbot, it is not the only app that uses NLP and AI to interact with users. In this post, we will explore some of the other apps like Chat GPT that use similar technologies to offer various functionalities.
Google Assistant
Google Assistant is a virtual assistant that uses NLP and AI to answer questions, perform tasks, and interact with users in natural language. It’s available on a range of devices, including smartphones, smart speakers, and smart displays. Google Assistant can help users with tasks such as setting reminders, checking the weather, and searching the web.
Google Assistant is different from other chatbots because it’s designed to perform specific tasks for users. It’s a great tool for people who want to automate their daily tasks and save time. Google Assistant is a great way to test the limits of AI and see how well it can perform specific tasks.
Amazon Alexa
Another virtual assistant uses NLP and AI to perform a range of tasks, including answering questions, playing music, and controlling smart home devices. It’s available on a range of devices, including the Amazon Echo smart speaker, Fire TV, and other smart devices. Alexa is designed to recognize natural language commands and respond in a conversational tone.
One of the unique features of Alexa is its ability to integrate with other apps and services, such as Uber, Spotify, and Amazon Music. This makes it a great tool for people who want to streamline their digital lives and control various devices and services from one central location.
Microsoft Cortana
Microsoft Cortana is your personal productivity virtual assistant that can help users with tasks such as setting reminders, checking the weather, and searching the web. Cortana helps you achieve more with less effort while allowing you to focus on what matters. It’s available on a range of devices, including Windows computers and smartphones.
Cortana uses natural language processing and machine learning to understand and respond to user input.
Cortana is developed by Microsoft and it uses the Bing search engine to perform tasks such as setting reminders and answering questions for the user. You already know how Microsoft has integrated ChatGPT technology in their Search engine and their browser, Microsoft Edge.
3. Computer Vision
What is Computer vision? It’s another important application of AI, which involves teaching machines to recognize and interpret images and video. Computer vision enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs — and take actions or make recommendations based on that information. Computer vision is used in a wide range of applications, including autonomous vehicles, facial recognition software, and medical imaging analysis.
Computer Vision Projects
Image Recognition Tools — identifying objects, people, and places in digital images or videos. Used in industries such as healthcare, security, and retail. For instance, healthcare providers can use image recognition tools to analyze medical images and identify diseases or abnormalities.
Facial Recognition — In the security industry, image recognition tools are used to identify and track criminals or suspicious activities, especially using Facial recognition technology. In eCommerce or retail, image recognition tools can be used to analyze customer behavior and personalize the shopping experience.
Computer Vision Examples
Social Media Marketing — All photo filters and animation filters in social media apps like TikTok or Snapchat, Google lens, and Night Sight, are some of the applications of computer vision.
Self-Driving cars Technology — With the use of computer vision, autonomous vehicles can understand their environment and navigate streets and highways on their own, swerve around obstructions, and get their passengers where they need to go safely. Tesla was the market leader in 2022, Mercedes, Volvo, BMW, and Lexus, among others.
Augmented & Mixed Reality — augmented reality apps rely on computer vision techniques to recognize surfaces like tabletops, ceilings, and floors which allows for Virtual items to be placed in the actual environment through augmented reality equipment like AR/VR Headsets (Oculus, Google Glass, Magic Leap, Lenovo ThinkReality, Holo Lens, among others).
Healthcare Industry — Automating the process of looking for malignant moles on a person’s skin or locating indicators in an x-ray or MRI scan is only one of the many applications of computer vision algorithms.
Internet — Image search, geolocalisation, image, and Video captioning, ariel imaging for maps, and video categorization are some computer vision applications.
How do Self-Driving Cars Work? (Self-driving cars explained)
Multiple cameras record the environment surrounding the vehicle, which is then sent into computer vision algorithms that analyzes the photos, locate road edges, decipher signposts, and see other vehicles, obstacles, and people. Self-driving car solutions that can work in Uganda include NVIDIA DRIVE End-to-End Platform for Software-Defined Vehicles among others.
Computer Vision Challenges
Fake News (Content) — Like all other technologies, computer vision in the wrong hands can lead to dangerous problems. Anybody with access to powerful data centers is capable of creating fake images, videos, or text content. If you have seen Barack Obama call Donald Trump a “complete dipshit”, or Mark Zuckerberg brag about having “total control of billions of people’s stolen data”, or Jon Snow’s moving apology for the dismal ending to Game of Thrones, those are some Deep fake examples.
Note: All the AI features, models, and applications, can work hand-in-hand to achieve various tasks. For example, Machine learning is part of deep learning and Natural language processing while Computer vision is directly related to deep learning as well.
4. Deep Learning
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign or to distinguish a pedestrian from a lamppost. It involves training neural networks with many layers to recognize patterns in data.
Deep learning has led to significant breakthroughs in areas such as image and speech recognition and has the potential to revolutionize many other areas of AI as well. One of the most exciting developments in the field of AI is the emergence of deep learning.
Deep Learning Applications
Law Enforcement — Deep learning algorithms can analyze and learn from transactional data to identify dangerous patterns that indicate possible fraudulent or criminal activity.
Financial Sector — Financial institutions regularly use predictive analytics to drive algorithmic trading of stocks, detect fraud, and help manage credit and investment portfolios for clients.
Customer Care — Many huge companies and organizations like Amazon and Google, use deep learning to help them with customer service. Services like Apple’s Siri, Amazon Alexa, and Google Assistant, are some examples of deep learning at work.
What are the uses of deep learning?
Computer Vision in Deep Learning — Computers can use deep learning techniques to comprehend images in the same way that humans do. It’s used in Content moderation to automatically remove unsafe or inappropriate content from image and video archives.
Predictive Analytics Tools — Predictive analytics is the use of statistical algorithms and machine learning techniques to identify patterns and make predictions about future events. Widely used in finance, marketing, and healthcare. For instance, banks can use predictive analytics tools to identify credit risk and prevent fraud.
AI In marketing — predictive analytics tools can be used to identify potential customers and develop targeted marketing campaigns. In healthcare, predictive analytics tools can be used to predict disease outbreaks and identify patients at risk of developing a disease.
In marketing, machine learning tools can be used to develop targeted advertising campaigns based on customer behavior and preferences. Applications can use deep learning methods to track user activity and develop personalized recommendations.
5. Sentiment Analytics Machine Learning
In simple terms, Sentiment analysis is the process of using natural language processing, text analysis, and statistics to analyze customer sentiment.
Sentiment analysis is a technique used to analyze and understand the emotions and opinions of customers. A popular way for organizations to determine and categorize opinions about a product, service, or idea. It is an approach to natural language processing (NLP) that identifies the emotional tone behind a body of text.
Sentiment analysis tools use machine learning algorithms to analyze text data, including social media posts, customer reviews, and feedback. This data can be used to improve customer service, develop marketing strategies, and enhance product development.
Top AI Companies, Applications, Tools & Examples
RapidMiner
RapidMiner is a data science platform that allows businesses to analyze and interpret large amounts of data, automate routine tasks, and improve decision-making. It includes tools for data preparation, model training, and deployment, and it supports multiple algorithms, including deep learning, decision trees, and clustering.
TensorFlow
TensorFlow is an open-source machine learning framework developed by Google. It allows you (developers) to create and train deep learning models for a wide range of applications, including image and speech recognition, natural language processing, and predictive analytics.
Robotic Process Automation (RPA) Tools
RPA is a technology that allows businesses to automate routine and repetitive tasks. RPA tools use machine learning algorithms and natural language processing to automate tasks such as data entry, data extraction, and data processing. RPA tools are widely used in industries such as finance, healthcare, and logistics.
For instance, in finance, RPA tools can be used to automate the processing of invoices and payments. In healthcare, RPA tools can be used to automate patient registration and data entry. In logistics, RPA tools can be used to automate order processing and tracking.
Keras
Keras is a high-level neural networks API written in Python. It provides an easy-to-use interface for building and training deep learning models, and it supports multiple backends, including TensorFlow, Microsoft Cognitive Toolkit, and Theano.
PyTorch
PyTorch is an open-source machine learning library developed by Meta AI (Formerly Facebook). It allows you (developers) to create and train neural networks (assist in deep learning) for a wide range of applications, including computer vision, natural language processing, and speech recognition.
Tip: A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain…
IBM Watson
IBM Watson is a suite of AI tools and services that help businesses to analyze and interpret large amounts of data, automate routine tasks, and improve decision-making. It includes tools for natural language processing (capable of answering questions posed in natural language), machine learning, and computer vision.
Amazon SageMaker
Amazon SageMaker is a cloud-based machine-learning platform that allows developers to build, train, and deploy machine-learning models at scale. It includes tools for data preparation, model training, and deployment, and it supports multiple frameworks, including TensorFlow and PyTorch.
Microsoft Azure ML
Microsoft Azure ML is a cloud-based machine learning platform that allows developers to build, train, and deploy machine learning models at scale. It includes tools for data preparation, model training, and deployment, and it supports multiple frameworks, including TensorFlow, PyTorch, and Keras.
H2O.ai
With the H2O AI Cloud, any individual data analyst, developer, or data scientist can make world-class AI models and applications in a matter of minutes, not years. It is the fastest, most accurate AI platform on the planet. It includes tools for data preparation, model training, and deployment, and it supports multiple algorithms, including deep learning, gradient boosting, and random forests.
Open AI
There are several companies and research organizations that are at the forefront of AI research and development. One of the most well-known is OpenAI, a non-profit research organization that is dedicated to creating and advancing AI in a safe and beneficial way. OpenAI has developed several advanced AI systems, including GPT-4, a language model that can generate human-like text (among other tasks) with a high degree of accuracy.
DeepMind by Google
Deep Mind is a team of scientists, engineers, and more committed to solving intelligence, advancing science, and benefiting humanity. It is a cutting-edge Artificial Intelligence company that is focused on developing advanced AI technologies for a wide range of applications. DeepMind has developed several AI systems, including AlphaGo, which famously defeated the world champion at the ancient Chinese board game Go, among other applications.
AI could be one of humanity’s most useful inventions. DeepMind
Will Artificial Intelligence Take Away my Job?
That’s a common question that many people have.
While AI has the potential to revolutionize many aspects of our lives, there are also concerns about its impact on society. One concern is that AI systems could take over jobs traditionally performed by humans, leading to widespread unemployment and economic disruption.
The answer is not simple, as AI may have different impacts on different jobs and sectors. Some studies suggest that AI could displace millions of jobs by 2051, while others argue that AI will create more jobs than it destroys.
Get Knowledgeable — It also depends on your skills, education and adaptability to new technologies.
Tip: If you are looking for a new job or want to upgrade your skills, you can check out some of the Best online platforms to learn about Artificial Intelligence (AI).
Others worry about the potential for AI to be misused, either intentionally or unintentionally, leading to unintended consequences that could be harmful to society.
To address these concerns, many researchers and policymakers are calling for increased investment in AI safety research, as well as for the development of ethical guidelines and standards for the use of AI.
Organizations such as the Partnership on AI are working to promote responsible AI development and deployment, with a focus on ensuring that AI technologies are developed and used in a way that is safe, transparent, and beneficial to society.
Conclusion
In conclusion, AI tools are transforming industries by automating routine tasks, enhancing decision-making, and improving customer experiences. The above-mentioned AI tools are just a few examples of the many powerful tools and applications available to businesses and organizations looking to leverage AI to drive innovation and growth.
Chat GPT, among other AI models, is an impressive AI-powered chatbot that has revolutionized the way humans interact with machines and more specifically with browsers (with AI-powered Bing search engine and browser —Microsoft Edge).
However, ChatGPT is not the only app that uses NLP (Natural Language Processing) and AI to interact with users. Cleverbot, Replika, Mitsuku, Google Assistant, Amazon Alexa, IBM Watson Assistant, and Xiaoice are all excellent examples of other apps that use similar technologies to offer various functionalities.
These apps are designed for different purposes, ranging from entertainment to customer service to emotional support. However, they all share the common goal of using NLP and AI to offer personalized interactions with users.
As AI and NLP technologies continue to evolve, we can expect to see even more impressive chatbots, virtual assistants, and other AI innovations and applications in the future.
