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Artificial Intelligence: Good AI or Bad AI

Last Updated on November 25, 2023 by SPN Editor

The question that often arises in our classrooms – “Is science a boon or a bane?” – is now being asked about Artificial Intelligence (AI). Artificial Intelligence can be classified as “Good AI” or “Bad AI” based on its application, design, and societal impact.

Good AI

Good AI comprehends user expectations and delivers the appropriate product or service.
It can assist users in discovering content that appeals to them.
Good AI encompasses efficiency through task automation, data analysis for informed decisions, assistance in medical diagnosis, and the progression of autonomous vehicles.
A multidisciplinary team of researchers at UC Davis is striving to develop “Good AI” that empowers users with greater control over their privacy and the content they’re recommended.

Bad AI

Bad AI fails to understand human expectations and displays unwanted promotions in inappropriate places.
It raises significant privacy concerns, and there is mounting evidence that individuals are being radicalized by some of the recommended content they consume online.
The drawbacks of AI include job displacement, ethical concerns about bias and privacy, security risks from hacking, and a lack of human-like creativity and empathy.
The risks of AI encompass automation-induced job loss, deepfakes, privacy infringements, algorithmic bias caused by flawed data, socioeconomic inequality, market volatility, weapons automatization, and uncontrollable self-aware AI.

It is important to note that the distinction between Good AI and Bad AI isn’t always straightforward and often depends on how the technology is used and regulated.

Good AI: A Game Changer Across Industries and Everyday Life

Artificial Intelligence (AI) is no longer a concept of the future. It’s here, transforming industries, enhancing services, automating tasks, and improving our daily lives. Here’s a look at how Good AI applications are making a positive impact across various sectors:

Revolutionizing Manufacturing with AI Robots

AI-powered robots are revolutionizing the manufacturing industry by automating processes, increasing efficiency and productivity, and predicting potential downtime and accidents.

For instance, FANUC, a leading supplier of AI-powered robots, offers robots that can work alongside humans on the production line, performing tasks such as assembling parts, painting products, and inspecting quality. These robots use AI to learn from their experiences, improving their performance over time.

Another example is ABB’s YuMi robot, which is designed for small parts assembly. It uses AI to analyze sensor data and adapt its movements in real-time, ensuring precision and efficiency in tasks such as fitting together small parts in electronics manufacturing.

AI-powered robots also play a crucial role in predictive maintenance. For example, General Electric uses AI-powered robots to monitor and analyze the condition of industrial equipment. These robots use AI to analyze sensor data, predict potential equipment failures, and alert operators so that maintenance can be scheduled before a failure occurs. This not only improves efficiency but also reduces costs associated with unplanned downtime.

In the realm of warehouse management, companies like Amazon use AI-powered robots for inventory management. These robots move around the warehouse, scanning QR codes on the floor to track their location and picking up shelves of products. They then bring these shelves to human workers, reducing the time and effort required for inventory management.

Moreover, Boston Dynamics’ Spot robot uses AI to navigate complex environments, performing tasks such as inspecting equipment and carrying out safety checks in a variety of industrial settings. Spot uses AI to analyze sensor data and make decisions in real-time, allowing it to adapt to new situations and perform tasks more efficiently.

Steering the Future with Self-Driving Cars

Artificial Intelligence (AI) is indeed the driving force behind autonomous vehicles, enabling them to navigate and make real-time decisions. To elaborate, these self-driving cars are equipped with a variety of sensors that collect data about the vehicle’s surroundings. This data can include information about other vehicles, pedestrians, road signs, and more.

For instance, Tesla’s Autopilot system uses a combination of cameras, ultrasonic sensors, and radar to perceive the environment around the vehicle. The data collected by these sensors is then processed by an onboard computer system, which uses AI algorithms to interpret the data and make decisions.

These AI algorithms often involve the use of machine learning and neural networks. Machine learning allows the system to learn from experience. For example, Waymo, Google’s self-driving car project, has driven millions of miles on public roads. The data collected during these drives is used to train machine learning models, enabling the system to handle a wide range of driving situations.

Neural networks, a type of machine learning model inspired by the human brain, are particularly useful for processing complex data such as images. For example, a neural network might be used to process the images captured by a car’s cameras, enabling it to recognize objects such as other vehicles, pedestrians, and traffic lights.

In various situations, these Good AI systems must make real-time decisions. For instance, if a pedestrian steps onto the road, the system must quickly decide whether to slow down, stop, or swerve. These decisions are made based on the data collected by the sensors and the predictions made by the AI algorithms.

Making Life Easier with Smart Assistants

Smart assistants such as Siri, Alexa, and Google Assistant are indeed revolutionizing our daily lives with their ability to perform a multitude of tasks. They leverage Artificial Intelligence (AI) to understand and respond to user commands, interact with users, and execute tasks on their behalf.

For instance, consider Siri, Apple’s voice-activated virtual assistant. Siri uses AI to understand natural language queries and commands. For example, if you ask Siri about the weather, it understands your request, fetches the relevant information from the internet, and presents it to you in a conversational manner.

Similarly, Alexa, Amazon’s virtual assistant, not only plays music or provides weather updates but also controls smart home devices. For example, you can command Alexa to dim the lights or adjust the thermostat, and it will execute these tasks by communicating with the respective smart devices.

Google Assistant is another prime example that uses AI to carry out tasks like setting alarms, sending texts, or even making reservations at restaurants. For instance, with a simple command like “Hey Google, set an alarm for 7 AM,” Google Assistant understands the task and sets the alarm accordingly.

These smart assistants are designed to understand natural language, which means they can interpret and respond to a wide range of user commands, even if they’re phrased in different ways. For example, whether you ask Siri “What’s the weather like?” or “Do I need an umbrella today?”, it understands that both commands are asking for weather information.

Moreover, these assistants adapt to user preferences over time using machine learning algorithms. They learn from each interaction, gradually improving their ability to predict and respond to user requests. For example, if you frequently ask Alexa to play a particular genre of music, over time, it will learn your preference and might even suggest songs from that genre.

Transforming Healthcare Management

Artificial Intelligence (AI) is indeed making significant strides in healthcare, transforming various aspects from patient scheduling to interpreting medical images for diagnosis.

For instance, AI can streamline patient scheduling. Companies like Zocdoc use AI to match patients with the right healthcare providers based on factors like location, specialty, and insurance. This not only saves time but also helps ensure that patients receive the most appropriate care.

In terms of interpreting medical images, AI can assist radiologists in diagnosing diseases. For example, Aidoc, an AI healthcare company, provides radiologists with advanced tools to detect acute abnormalities across the body, helping prioritize patient treatment. Similarly, Google’s DeepMind Health has developed an AI system that can quickly and accurately detect eye diseases from complex optical coherence tomography (OCT) scans.

AI also offers opportunities to reduce human error. In medication management, for example, AI can cross-verify prescriptions against a patient’s health records to flag potential drug interactions or allergies, which can be overlooked due to human error.

AI can also assist medical professionals in various ways. IBM’s Watson for Oncology uses AI to analyze a patient’s medical information and provide evidence-based treatment options, assisting oncologists in making informed treatment decisions.

Moreover, AI can offer round-the-clock patient services. Chatbots like Babylon Health’s symptom checker can provide medical advice based on the symptoms described by the user. These chatbots use AI to understand the user’s input, compare it to a database of diseases, and provide potential diagnoses.

As AI tools continue to evolve, we can expect even more applications. For instance, AI is being used to read medical images. Zebra Medical Vision, an AI health tech company, has developed algorithms that can read and diagnose medical imaging data, such as CT scans and X-rays.

AI can also help in diagnosing medical problems. PathAI is developing machine learning technology to assist pathologists in making more accurate diagnoses. Their AI-powered platform helps identify patterns in diseases and predict patient outcomes.

Furthermore, AI can aid in creating treatment plans. Tempus uses AI to analyze clinical and molecular data and generate personalized treatment plans for cancer patients.

Streamlining Finance with Automated Investing
AI is reshaping the finance sector by analyzing market trends and making investment decisions. AI investing uses algorithms and computer programs to make investment decisions based on data analysis, risk assessment, and market trends. This eliminates the need for human decision-making, allowing for faster, more efficient investment management.

Personalizing Travel with Virtual Booking Agents
AI is changing the way we travel by providing personalized recommendations and handling bookings. It collects and processes large amounts of data to detect preference patterns and suggest booking options accordingly.

Enhancing Business Insights with Social Media Monitoring
AI algorithms are used to analyze social media data, identify trends, and understand user preferences. This social listening gives businesses deeper insights into customer behavior and preferences, helping them tailor their strategies accordingly.

Improving Customer Experience with Marketing Chatbots
AI-powered chatbots are enhancing customer service by answering queries and offering product recommendations. They can solve quick problems for customers, nurture leads, answer repeated questions, and personalize the customer journey.

Simplifying Household Chores with iRobot’s Roomba
iRobot’s Roomba, a smart vacuum cleaner, uses AI to scan room size, identify obstacles, and remember the most efficient routes for cleaning. This smart device is an example of how AI is making our daily lives easier and more convenient.

Sophia: The AI Marvel from Hanson Robotics

Sophia, developed by Hanson Robotics, is an advanced social robot that has been making waves in the field of Artificial Intelligence (AI). She is capable of communicating using natural language and can express human-like emotions.

Sophia’s architecture is a blend of various advanced technologies. It includes scripting software, a chat system, and OpenCog, an AI system specifically designed for general reasoning. Sophia’s AI is a combination of cutting-edge work in several areas including symbolic AI, neural networks, expert systems, machine perception, conversational natural language processing, adaptive motor control, and cognitive architecture.

These examples illustrate the transformative power of Good AI. It’s automating tasks, enhancing services, and improving user experiences across a wide range of sectors. AI is not just transforming industries; it’s changing our everyday lives, making tasks easier and more efficient. As AI continues to evolve, we can expect even more innovative applications in the future.

Why do we call Artificial Intelligence as Bad AI?

Recently, there have been cases where lawyers have faced repercussions for using AI chatbots like GPT in their work.

Zachariah Crabill, a lawyer based in Colorado, utilized ChatGPT to assist him in drafting a court document. However, the tool incorporated numerous fictitious lawsuit citations in the document. Consequently, Crabill was reported to a statewide office that handles attorney complaints and was subsequently dismissed from the law firm he was working for.

In another instance, Steven Schwartz, a lawyer based in New York, employed ChatGPT for legal research for a personal injury case. The AI tool referenced several non-existent previous court cases. Schwartz and his firm were penalized US$5,000 for submitting false citations in a court filing.

These incidents highlight the potential risks and ethical considerations of using AI tools in professional settings, particularly in fields like law where accuracy and authenticity are of utmost importance.

The Hidden Hurdles of AI in Image Recognition

Despite the significant strides made by deep learning, a subset of AI, in image recognition, it’s not without its flaws. A study revealed that even the most advanced computer vision algorithms struggled to accurately interpret a set of 7,500 raw nature images, highlighting the challenges AI faces in understanding and interpreting real-world, unstructured data.

Microsoft’s Tay’s Misadventure

Microsoft’s chatbot, Tay, was designed to improve machine-human conversations. However, within 24 hours of learning from human interactions, Tay began posting inappropriate comments on Twitter. This incident underscores the risk of AI systems learning and amplifying the negative aspects of human behavior.

A Glimpse into Amazon’s Hiring Process

Amazon tried to automate its hiring process using AI. However, the system developed a bias, favoring certain demographics. This incident highlights the danger of AI systems perpetuating existing biases present in the training data.

The Dilemma of AI Transparency and Explainability

AI and deep learning models can be complex and difficult to understand, resulting in a lack of transparency in AI’s decision-making process. This is a significant issue, especially in sectors where explainability is crucial, such as healthcare and finance.

The Socio-Economic Implications of AI Automation

As AI technology permeates various industries, concerns about job automation leading to significant job losses are escalating. This socio-economic issue necessitates policy-level interventions.

The Abuse of AI Algorithms in Social Manipulation

The proliferation of fake news and misinformation via AI algorithms is a rising concern, emphasizing the need for robust mechanisms to prevent the misuse of AI technology.

Moreover, Deepfakes and AI are increasingly being weaponized by fraudsters, ushering in a new era of complex crimes. Scammers are leveraging AI to fabricate highly convincing deepfakes, including computer-generated voices that can fool even their closest kin, and masks derived from social media images capable of circumventing face ID protected systems.

Once confined to the realm of science fiction, these techniques are now employed in a variety of scams, including sextortion, financial fraud, and the dissemination of fake news. The incorporation of AI has supercharged fraud, resulting in a substantial surge in financial crimes. In the US alone, consumer losses amounted to nearly $8.8 billion last year, a 44% increase from 2021.

Fraudsters are exploiting the buzz around AI to deceive investors. They use AI-related jargon to entice investors into fraudulent schemes. For example, they might boast about an AI system capable of predicting stock market trends with high precision, promising implausible returns.

Furthermore, the emergence of deepfake technology, powered by AI, has given rise to imposter scams. Fraudsters can generate ultra-realistic fake videos or voice recordings of CEOs or other corporate officials to manipulate stock prices. They disseminate false information about a company’s financial status or impending deals, causing stock prices to swing. Investors who act on this misleading information end up incurring losses.

AI is also being harnessed to execute “synthetic” fraud. In this kind of scam, criminals pilfer personal information to fabricate fake identities, which they use to swindle financial institutions. Assisted by AI, they can scour the internet at unprecedented speeds to collect information, making the scams more rapid and sophisticated.

These examples demonstrate the potential risks and adverse impacts of AI. These issues often arise from how the technology is used and regulated, rather than the technology itself. Hence, it is very important to establish appropriate regulations and ethical guidelines for the use of Artificial Intelligence as Good AI.

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