Robotics and engineering have been connected for a long time, but it’s only now that we’re beginning to see the true impact that industrial-scale implementations of this technology can have in this context.
To illustrate the scope of the revolution in engineering that’s being enabled by robots, here is an overview of the main things you need to know.
Industrial robots are autonomous machines capable of performing tasks without human intervention. These versatile tools are widely applied across various industries, including in manufacturing and assembling processes.
The magic of these mechanical maestros lies in their programmability, as you can reassign them to perform different functions according to your engineering needs. This is particularly important in the current climate, where efforts to roll out industrial robots to smaller businesses and even individual hobbyists are ongoing.
In fact we’re seeing industrial robots costs continue to fall as adoption increases, making them viable for more engineering projects by the week. This affordability has opened up countless possibilities in automating complex tasks that were traditionally performed manually.
There are a number of specific areas in which industrial robots have advantages to offer in an engineering context. The headline perks include:
Automation and Efficiency: Robots are excellent for executing repetitive tasks that could be tedious or even hazardous for humans. Through intelligent automation , they significantly boost operational efficiency while also mitigating risks.
High-Quality Output: The meticulous precision associated with robotic systems translates into uniform, high-quality output, which is particularly appreciated in industries like automobile manufacturing where consistent quality is paramount.
Cost Savings: Despite their initial costs, which are coming down as mentioned, industrial robots present long-term financial benefits. They can complete tasks quickly and accurately around the clock, leading to substantial labor-related cost savings over time.
Lastly, industrial robotics tech is not just treading water, but constantly powering ahead to deliver bigger and better opportunities for engineers and organizations. From rapid prototyping to mass production and beyond, the potential is immense.
As groundbreaking as robotic automation is, it presents a unique blend of opportunities and challenges. First, let’s talk a little more about what they can help to unlock:
Unprecedented Operational Efficiency: The power to complete tasks speedily and efficiently means companies can increase their output capacity without making compromises on quality. This operational efficiency elevates profitability while providing an essential competitive advantage.
Reshaping Workforce Skills: As robots take over manual tasks, employees can focus on higher-value activities that demand creativity or strategic analysis. Of course this shift necessitates reskilling and upskilling , so it is not unambiguously appealing, especially for those in relatively low-skill roles right now.
It’s also necessary to recognize that some challenges loom large in the wake of this revolution:
Initial Investment & Implementation Complexity: Even with costs shrinking, it’s still necessary to splash out to secure the latest industrial robots. Then there are the complexities of the initial implementation stages, which can pose an obstacle to integration.
Dependence on Power Access & Maintenance: Robots require a continuous power supply to remain operational, meaning that unplanned downtime due to factors outside of your control is a likelihood. Meanwhile ensuring their proper functioning requires preventive maintenance routines, which means procurement comes with an ongoing commitment to careful upkeep.
It’s important to get both sides of the story before deciding whether to adopt industrial robots for engineering projects. Making an informed decision is better than jumping on the hype train without proper planning.
Industrial robots can already be seen up and running in various places, so here are just a few instances of their successful application to ongoing projects:
Automotive Manufacturing: Car manufacturers routinely deploy robots for arduous tasks like welding, assembly, painting, and even quality control, leading to increased productivity while reducing human injuries.
Construction Industry: Some companies now utilize brick-laying robots to streamline operations, overcoming manpower shortages and also coping with concerns over hazardous working conditions.
Healthcare: Surgical robotic systems allow doctors precise manipulation during complex surgical procedures, enhancing their abilities to operate safely on patients and even automating aspects as well. They sit alongside other technological breakthroughs impacting this sector.
From factories and building sites to hospital rooms and beyond, industrial robots are steadily arriving across various operational avenues. These examples present just a small glimpse into the multifaceted capabilities offered by our autonomous allies.
Building on lessons from the past and present, we can anticipate certain key trends that will dictate the future of industrial robots in engineering. These predictions shed light on how technology might continue to shape our world:
Advanced AI Capabilities: With growing artificial intelligence capabilities, robots could take on more complex problem-solving tasks independently, increasing their versatility.
Collaborative Robots: Known as 'cobots', these are designed to physically interact with humans within a shared workspace. Cobots are safer and more flexible than traditional industrial robots, so may soon become more commonplace.
Eco-Friendly Practices: As environmental concerns mount globally, expect future developments in robotics to prioritize sustainability goals. This includes reducing material waste and moving away from carbon-emitting machinery to robots powered by renewable energy.
The road ahead may not always be obvious, but in terms of industrial robots, there is little question that their role in engineering will expand and become more closely intertwined with what experts in the field do from day to day.
All of this should paint industrial robots as an unambiguously revolutionary technology, not only for engineering but more generally for business and society at large.
There are those who fear what the robotization of manual tasks might mean for humans, but it seems more likely that this will improve things for workers across the skills spectrum. Whether we’re talking about taking tedious tasks off the table altogether or dramatically enhancing workplace safety, this is a change that should be celebrated rather than treated with suspicion.
"Unveiling the Battle: Generative AI vs Adaptive AI"
Artificial Intelligence (AI) is a rapidly evolving field, with two main approaches capturing attention: Generative AI and Adaptive AI. These techniques offer unique capabilities and have the potential to revolutionize various industries.
In this article, we will explore the fundamental principles, methodologies, applications, limitations, ethical considerations, and prospects of Generative AI and Adaptive AI. By gaining a deeper understanding of these approaches, readers will be better equipped to assess their relevance and make informed decisions.
"The Power of Creation: How Generative AI Works"
Generative AI focuses on the creation of new and original content. It utilizes advanced algorithms, such as deep learning models and recurrent neural networks, to learn patterns from vast datasets and generate outputs resembling human-created content.
From generating artwork to composing music, Generative custom AI development company enables creative expression and pushes the boundaries of what machines can achieve. By comprehending the workings of Generative AI, we can appreciate its potential for innovative applications.
The cost to develop generative AI can range from tens of thousands to several hundred thousand dollars, depending on project complexity and scope.
According to industry statistics, the average cost to build generative AI systems can be estimated as follows:
According to Forbes, Generative AI Breaks The Data Center: Data Center Infrastructure And Operating Costs Projected To Increase To Over $76 Billion By 2028.
"Adaptability at Its Finest: Understanding Adaptive AI"
Adaptive AI emphasizes the ability of AI systems to learn and adapt based on feedback and changing circumstances. Through techniques like reinforcement learning and evolutionary algorithms, Adaptive AI models improve their performance by continuously acquiring knowledge and adjusting their behavior.
This approach finds applications in dynamic environments where flexibility and responsive decision-making are crucial. By diving into Adaptive AI, we can grasp its adaptive mechanisms and impact on various domains.
The average cost to build adaptive AI can range from $500,000 to several million dollars.
According to industry statistics, the average cost to build adaptive AI systems can be estimated as follows:
According to Gartner: Gartner expects that by 2026, enterprises that have adopted AI engineering practices to build and manage adaptive AI systems will outperform their peers in the number and time it takes to operationalize artificial intelligence models by at least 25%.
"Head-to-Head: Generative AI vs Adaptive AI"
Let's compare the key aspects of Generative AI and Adaptive AI in a concise bullet point format:
Focuses on creating new and original content.
Utilizes algorithms like deep learning models and recurrent neural networks.
Learns patterns from vast datasets to generate human-like output.
Thrives in creative domains such as art, music, and writing.
Pushes the boundaries of machine-generated creativity.
Emphasizes learning and adaptability in dynamic environments.
Utilizes techniques like reinforcement learning and evolutionary algorithms.
Improves performance through continuous learning and adjustment.
Excels in tasks that require flexibility and responsive decision-making.
Finds applications in optimization, prediction, and personalization.
Several key factors influence the cost of developing both generative AI and adaptive AI systems. These factors include:
"Real-World Implications: Where Generative AI and Adaptive AI Excel"
Use Cases and Applications of Generative AI:
Generative AI has found diverse applications across industries, including:
Computer-generated art and design
Music composition and generation
Creative writing and storytelling
Virtual and augmented reality experiences
Product and logo design
Fashion and textile design
Video game content generation
Content creation for marketing and advertising
Generative AI enables creative professionals and industries to explore new realms of artistic expression and leverage the power of AI to generate unique and compelling content.
Adaptive AI has demonstrated its value in various domains, including:
Personalized recommendations in e-commerce and streaming platforms
Dynamic pricing and demand forecasting in retail and hospitality
Fraud detection and risk assessment in finance and insurance
Autonomous vehicles and intelligent transportation systems
Predictive maintenance in manufacturing and logistics
Healthcare diagnostics and personalized treatment plans
Natural language processing and chatbots for customer service
Personalized learning and adaptive education platforms
"Roadblocks and Hurdles: The Limitations of Generative AI and Adaptive AI"
Maintaining consistent quality and coherence in generated content is a challenge.
Ensuring genuine creativity and originality in output can be difficult.
Generative AI heavily relies on high-quality and diverse training data.
Evaluation and validation of generated content can be subjective and challenging.
Long training times and resource-intensive computational requirements can limit scalability.
Ethical considerations arise regarding ownership and potential misuse of AI-generated content.
Limited control over output and interpretability can lead to unpredictable results.
Balancing exploration and exploitation in the learning process poses a challenge.
Generating high-quality and realistic content is an ongoing challenge for generative AI systems.
Addressing biases and ensuring fairness in AI-generated content is a complex task.
Dependence on the training data's quality, relevance, and representativeness for effective learning and adaptation.
Vulnerability to bias and skewed outcomes if the training data is unbalanced or contains inherent biases.
Ethical concerns related to privacy, transparency, and potential reinforcement of societal biases.
Complex implementation and tuning processes require careful calibration and monitoring.
Balancing the need for adaptability with the need for stability and reliability in critical decision-making scenarios.
Ensuring continuous learning and adaptation in dynamic and evolving environments.
Overcoming the limitations of data availability and quality for effective model updates.
Adapting to changing user preferences and behaviors in personalized recommendation systems.
Addressing the "cold start" problem when dealing with new or rare instances.
Balancing exploration and exploitation to achieve optimal performance in reinforcement learning scenarios.
Big companies are leveraging generative and adaptive AI technologies to gain a competitive edge and deliver exceptional experiences. Here are notable examples:
Google's DeepMind: DeepMind's language models like GPT-3 generate human-like text, enabling content creation and virtual assistants.
Netflix: Adaptive AI personalizes the user experience, recommending tailored content based on viewing patterns and preferences.
Amazon: Alexa uses generative AI for natural-sounding responses, while adaptive AI powers product recommendations.
Adobe: Adobe Sensei's generative AI features automate design variations and enhance graphics creation.
Facebook: Generative AI generates alternative text for images, while adaptive AI personalizes news feeds.
Let's explore some notable examples of how these technologies are being utilized by prominent organizations:
IBM: IBM's Watson AI platform utilizes generative AI to generate natural language responses, engage in intelligent conversations, and assist in various domains such as healthcare, finance, and customer service.
OpenAI: OpenAI's language models like GPT-3 are employed by big companies to generate content, draft emails, provide customer support, and create chatbots.
Autodesk: Autodesk's generative design tools use AI algorithms to explore numerous design options and help professionals optimize their designs and generate innovative solutions.
NVIDIA: NVIDIA's generative AI solutions, such as generative adversarial networks (GANs), are used in image generation for design, advertising, and virtual environments.
Adobe: Adobe incorporates generative AI into its creative software suite, enabling artists, designers, and content creators to enhance images, remove unwanted elements, and automatically generate content.
"Beyond the Present: The Evolution of Generative AI and Adaptive AI"
The future of AI holds exciting possibilities as Generative AI and Adaptive AI continue to evolve. The evolution of Generative AI and Adaptive AI shapes the future of AI.
Generative AI is advancing to produce highly creative and original content.
Adaptive AI focuses on adaptability and responsiveness, enabling personalized experiences.
The convergence of Generative AI and Adaptive AI holds immense promise.
Hybrid models combining creativity and adaptability will revolutionize industries.
Privacy, fairness, and transparency are essential considerations in the future of AI.
Ongoing research and collaboration are crucial for addressing ethical challenges.
The future of AI promises a transformative world of innovation and possibilities.
"Choosing Your Path: Which AI Approach is Right for You?"
When considering AI, the choice between Generative AI and Adaptive AI depends on individual requirements and objectives. Generative AI suits those seeking creative exploration, while Adaptive AI suits those valuing adaptability and personalization. As the AI landscape evolves, hybrid models may emerge, providing the best of both worlds. Embrace the future of AI and select the path that aligns with your goals to drive innovation and transformative change.
Generative AI is a remarkable innovation, and the results of its use are captivating. Tools like DALL-E and ChatGPT have rapidly transitioned from research labs into the mainstream. They are widely discussed on social networks, used by both professionals and laypeople, and their outputs – be it texts, images, or code – resemble human creations remarkably.
According to Statista , the generative AI market will reach $207 billion by 2030. It will show an impressive annual growth rate of 24.4% between 2023 and 2030. Another source, MarketResearch.Biz, expects the size of the generative AI market in software development to hit $169.2 billion by 2032. However, due to the rapid pace of current advancements, it's challenging to predict exact figures; this technology is expanding at breakneck speed.
If harnessed effectively, generative AI in software development could soon become commonplace. It's widely used now, and in the future, it may become a necessity for IT professionals worldwide.
McKinsey & Company recently conducted an extensive study to explore the influence of this innovative technology on the work of developers. The researchers assembled a lab with over 40 specialists from different countries, who had various levels of experience and expertise. For several weeks, participants completed common coding tasks in the following areas: generating new code, refactoring existing code, and documentation.
There were two groups performing the above activities. One of them could use two leading generative AI tools, while the other had to work without AI assistance. The study collected quantitative timing data, task surveys, code quality assessments, and participant feedback.
The results reveal that when properly utilized, this technology can markedly quicken numerous everyday coding jobs. IT specialists reduced code documentation time by almost 50% through collaboration with intelligent software. They were also about 35-45% faster at writing new code and about 20-30% faster at improving existing code.
Yet, McKinsey found that getting productivity gains requires thoughtful implementation. The time savings declined for demanding tasks, especially among junior developers. But with the right human oversight, code quality did not suffer – it even slightly improved in some areas like readability.
The study highlights the importance of generative AI for developers but sees is as a tool rather than a replacement. To ensure quality, prompt engineering skills are essential to guide AI properly.
While speedy code generation grabs headlines, McKinsey found major productivity gains across documentation, refactoring, and more. However, the technology is still most suitable for basic prompts, not complex coding challenges.
McKinsey's research indicates that generative AI in software development promises to significantly boost the productivity of IT professionals if thoughtfully leveraged. But realizing this potential will require investments in prompt engineering skills, use case selection, risk management, and more.
Let's focus on coding tasks where generative AI demonstrates particular promise. Smart tools excel at handling repetitive manual work – quickly generating boilerplate code so that developers can focus on higher-value challenges. AI also facilitates drafting new code, giving hints on how to overcome writer's block. For updating existing code, it can rapidly implement iterations when given proper prompts.
When software engineers encounter unfamiliar coding challenges, AI ensures quick upskilling. It can provide explanations for new concepts, compare different pieces of code, and deliver tutorials on frameworks to help engineers quickly grasp the required knowledge. This enhanced knowledge helps IT professionals to take on more complex assignments.
Four prime areas where smart technology is of great help are:
AI quickly creates standard code, functions, and documents, saving developers from boring work and making them much faster and more productive.
Starting new projects
Smart tools help get past the problem of not knowing where to start. They suggest code when you describe what you want to do. This makes you more creative and helps you work faster.
Simplifying changes
With specific modifications in mind, developers can use AI to improve existing code rapidly. This speeds up improvements.
Learning new things
When working on something new, AI offers tutorials, examples, and explanations to help you learn quickly. This makes you more productive on new projects.
In simple terms, generative AI in software development makes humans better at coding.
It’s hard to question the usefulness of generative AI for developers. However, human expertise is critical in several key areas. These are:
Error detection
Human programmers remain indispensable in scrutinizing code for bugs and errors. Researchers identified situations where smart tools gave inaccurate suggestions and even made critical mistakes. Thus, one expert had to input multiple prompts to rectify an erroneous assumption made by AI. Another programmer described the need to painstakingly guide the tool through the debugging process to secure coding accuracy.
Contextual insight
Ready-made smart tools possess coding knowledge, but they lack awareness of the unique requirements of specific businesses. Understanding such context is vital for qualitative work to ensure seamless integration with other software solutions, adherence to key standards, and the fulfillment of users’ requirements. Professional human developers furnish AI with contextual information. They specify how the code will be used, who the end-users are, the systems it will interact with, data considerations, and more.
Complex problem-solving
AI in software development excels at handling straightforward prompts, including code snippet optimization. However, when faced with intricate coding requirements, like merging multiple frameworks with distinct code logic, human professionals demonstrate their superiority. Generative technology becomes less useful as problems become more intricate and require a holistic approach.
So, high-quality coding still demands human intervention.
As the tech world keeps changing, there emerge many AI tools for developers. Let’s look at the most popular solutions:
ChatGPT
This no-cost application is a prime example of the vast potential of generative AI. While it may not be the ideal choice for coding-related assignments, it excels at generating boilerplate code, translating code into various languages, and automating routine tasks. It provides an excellent starting point for those looking to delve into the world of generative AI in software development.
GitHub Copilot
When talking about AI for developers, GitHub Copilot is one of the prime options. Powered by OpenAI Codex, which has undergone extensive training on diverse codebases, this tool provides precise code recommendations tailored to your project's requirements and stylistic preferences. It proves particularly useful for programming in languages such as Python, JavaScript, and more.
Google Bard
This application is compatible with 20 programming languages, capable of producing code based on your inputs and comments, elucidating code, and aiding in code modifications. Moreover, it comes at no cost.
Auto-GPT
This tool aims to make GPT work more independently. It breaks big tasks into smaller ones and uses multiple GPT instances to handle them. This can make it more efficient for complex projects.
Amazon CodeWhisperer
Amazon's tool recommends code by analyzing your prompts, comments, and project code. It excels when it comes to coding that involves AWS APIs such as EC2, Lambda, and S3.
Tabnine
Tabnine is an additional AI coding companion leveraging OpenAI Codex. It is good at auto-completing lines of code or even entire functions, and it seamlessly aligns with the code style of your project. What sets it apart is its compatibility with a wide array of applications.
CodeWP
This tool is highly effective for WordPress development, producing PHP, Javascript, and jQuery code that seamlessly integrates with WordPress, its associated plugins, and databases. Despite its relatively recent introduction, it receives regular updates to enhance its capabilities.
What the Diff
This tool streamlines the process of code review and documentation by examining disparities in code and producing concise summaries using simple language. It proves beneficial for keeping non-technical team members in the loop and enhancing documentation quality.
Text-to-image tools
Applications such as DALL-E 2, Stable Diffusion, and Midjourney are capable of producing images based on textual prompts, a valuable feature for crafting front-end design components and creating image placeholders.
Remember to be cautious, though. Many big companies have concerns about how GPT and similar tools handle sensitive data, and these tools aren't completely independent yet. While they boost productivity, they don't replace the role of human engineers, at least not right now.
Generative AI in software development has the immense potential to transform workflows and significantly boost productivity. However, realizing these benefits requires thoughtful implementation tailored to each organization's unique requirements.
Professionals involved in the development of custom solutions should emphasize ethical AI practices, continuous training, and the adoption of new approaches to smart technology. Implementing robust human oversight mechanisms is crucial. When human developers and AI tools work together diligently, we can boost productivity and reduce risks. The future looks good for those who use generative AI carefully and responsibly.
Note: Written by Valentin Kuzmenko, VP of Sales at Andersen.
Artificial intelligence is becoming more and more important for data security. In this post, we'll look at how AI may assist businesses in anticipating and thwarting threats. But before going ahead we will explain the terms artificial intelligence and machine Learning.
Artificial intelligence (AI) is a discipline of computer science that focuses on making electrical equipment and software intelligent enough to do human activities. AI is a broad concept and a basic subject of computer science that may be used to a variety of domains including learning, planning, problem solving, speech recognition, object identification and tracking, and other security applications.
Artificial intelligence is divided into numerous subsets. We shall look at two of them in this article:
Deep Learning
Machine Learning
Machine learning (ML)-based computer systems have the capacity to learn and carry out tasks without explicit instructions. These systems find, examine, and comprehend data patterns using ML algorithms and statistical models. Many jobs that are typically completed by people are now routinely carried out automatically using machine learning capabilities.
A machine learning technique called unsupervised learning enables ML algorithms to carry out tasks without clear instructions and produce desired results. Based on analysis and experience, this method determines the best solutions to a problem. When given an input (a task to perform), the model can decide on its own what the optimum course of action is. The model gets better trained and becomes more effective the longer it solves the assignment.
The benefit of ML for many tasks is obvious—machines don't grow bored or upset by repeatedly performing the same monotonous tasks. By automating numerous processes in work chains, they also drastically reduce workloads. Security teams can, for instance, use AI-based solutions (which will be covered later) to automatically detect threats and handle part of them, minimising the amount of human contact necessary for specific security activities.
Data anomalies can be found with the aid of machine learning. You may train algorithms that recognise particular patterns and user behaviour using machine learning. Detecting suspicious behaviour in a workplace, such as an increase in password resets or unexpected requests for sensitive data, will be made possible thanks to this.
Computer vision can also be used to find data trends that might point to a possible system or network vulnerability management violation. Machine learning techniques are employed to forecast future examples of this behaviour based on the environment's present conditions after being trained with historical data on previous successful attacks (e.g., usage patterns).
Besides ML techniques you can rely on the use of VPN. Because you can keep your data from suspicious activities from hackers by installing a VPN. It is easy to set up a VPN on router and once you set it will start monitoring your PCs activities against malicious attacks.
Before an attack ever occurs, AI may identify it and stop it. Understanding how data is gathered, processed, and presented is just as important as looking at the data itself. AI is able to spot warning signals of impending attacks and stop them from executing in the cloud, on a network, or even in real time.
By seeing dangerous activity on your virtual machine (including malware) while you're away from home or work or even on mobile devices, AI can also assist you in protecting yourself against AI-enabled dangers of gadgets and PCs both! Additionally, there are social media platforms like Facebook and Twitter and AI also helps to keep them secure from attackers.
Artificial intelligence is becoming more and more important for data security. AI can assist businesses in identifying dangers, spotting abnormalities, and reaching decisions more quickly than ever before.
It plays a significant role in contemporary data management techniques, which in turn have significant ramifications for enterprises across all industries.
"Domain knowledge" is the capacity for people or computers to comprehend information and take appropriate action without being instructed on its workings or meaning (AKA: natural language processing).
"Machine learning" is the process through which computers or humans can perform jobs utilising data sets without any prior knowledge.
In order to learn from mistakes they made earlier in life and produce better results later on when things get difficult again, both of these strategies depend on increasing volumes of data being gathered over time.
Obtaining a thorough and accurate inventory of all devices, users, and software with access to computer systems. Inventory also heavily relies on categorization and the measurement of business criticality.
Hackers, like everyone else, follow trends, therefore what's popular with hackers changes on a regular basis. AI-based counterintelligence systems can provide current knowledge about global and industry-specific threats to assist in making crucial prioritising decisions on the basis not only on what may be employed to defend your organisation, but also on what is likely to be utilised to attack your organisation.
It is critical to comprehend the significance of the numerous security technologies and verification activities that you have implemented in order to keep a stable security posture. AI can assist you in determining your information security program's strengths and weaknesses.
Accounting for IT assets, threat sensitivity, and control efficacy, AI-based solutions may forecast how and where you will be compromised, allowing you to allocate resources and tools to areas of weakness. AI-derived prescriptive insights can assist you in configuring and improving policies and processes to most greatly increase your organisation's cyber resilience.
AI-powered systems can give greater context for prioritising and response to safety warnings, for quick incident response, and for surfacing root causes to remediate exposures and avoid future issues.
The explainability of guidance and analyses is critical to leveraging AI to enhance human information security teams. This is critical for gaining buy-in from stakeholders across the organisation, understanding the impact of various information security programmes, and reporting relevant information to all stakeholders involved, including end users, security operations, CISO, audit committees, CIO, CEO, and board of directors.
Although I have been doing this for a while, data security is currently enjoying a comeback. People are more worried than ever about their sensitive data being stolen because hackings are on the rise. The good news is that scalable data protection is possible with artificial intelligence (AI). In this article, we talked about how AI and machine learning combine to find abnormalities in massive datasets and spot trends that point to shady conduct.
"Artificial Intelligence is the science and engineering of designing intelligent machines, especially intelligent computer programs."
I have been teaching Artificial Intelligence to engineering students for five years and I normally assign them projects at the end of their course and the one, I really enjoyed was "virtual psychiatrist", designed by a group of 5 students. You can tell that robot your symptoms/condition and it will tell you the cure and measures. During it's evaluation, the virtual Psychiatrist asked "What's your problem?" I replied, "I am fine" but still it suggested numerous cures and several therapies. I laughed and told the students that this software will not qualify for the Turing test. Now, you must be thinking, what's a Turing Test, so let's have a look at it: