Week 2: Applications and Tools of Generative AI

1. IT and DevOps:
   • Code generation tools like GitHub Copilot and Synk’s DeepCode that assist developers in writing code.
   • AI-powered code review tools that analyze code repositories and coding standards to enhance code quality.
   • Generative models that generate synthetic test cases and test data to improve software efficiency and reliability.
   • Tools like IBM’s Watson and Moogsoft that analyze system logs and metrics to detect anomalies and prevent critical failures.
2. Entertainment:
   • Generative AI tools used in game development to create unique characters, animations, and virtual reality experiences.
   • Tools that generate synthetic content like music, scripts, stories, videos, and video games.
   • Translation and personalization of content to cater to different audiences.
3. Education:
   • Generative AI used to create personalized learning experiences and adaptive learning journeys.
   • Language translation tools that make educational content accessible in different languages.
   • Generative algorithms that generate assignments and provide instant feedback to learners.
   • Knowledge tracing algorithms that track learner progress and adapt content accordingly.
4. Banking and Finance:
   • Generative AI tools that auto-detect risks, generate insights, and make financially literate recommendations.
   • Tools that simulate potential fraud scenarios to detect credit risk and fraud risk.
   • AI-powered generative models that analyze news articles and social media for market sentiment analysis and portfolio management.
5. Healthcare:
   • Generative AI models that improve the robustness of deep learning models used for medical image analysis.
   • Tools that generate synthetic data for rare medical conditions to facilitate research and develop diagnostic tools.
   • Conversational AI systems that provide immediate medical advice and personalized treatment plans.
6. Human Resources:
   • AI-powered tools that automate HR tasks such as job requisitions, candidate screening, and employee onboarding.
   • Performance management tools that automatically generate performance documents and reviews.
   • HR analytics tools that provide valuable insights for decision-making and compliance.

Welcome to Applications
of Generative AI. After watching this video, you’ll be able to identify
some of the applications of generative AI in different
domains, specifically, the domains of IT and DevOps,
entertainment, education, banking and finance,
health care, human resources and on working
professionals in general. In IT and DevOps generative
AI helps improve software delivery processes and infrastructure management. The code generation capabilities
of generative AI reduce manual coding effort and time
spent on repetitive tasks. Generative AI-powered
code review tools such as GitHub Copilot and Synk’s DeepCode
examine code repositories and coding standards to enhance code quality and
maintainability. Generative models generate
synthetic test cases and test data that
simulate user behavior. Variations and test cases
impact software efficiency, reliability and robustness. Tools such as Applitools
and Testim guarantee adequate testing coverage by increasing the depth and
diversity of data sets. To monitor and detect anomalies in code, tools such
as IBM’s Watson, AIOps, and Moogosft, AIOps analyze system logs, metrics and other data. This helps with proactive maintenance
and troubleshooting, reducing downtime and
preventing critical failures. GitLab Duo supports the
continuous integration and deployment or CICD pipelines by automatically
creating release notes, changing logs, and updating deployment templates
and scripts. Other applications in IT and DevOps include natural
language interfaces, automated infrastructure
management, and predictive maintenance. Now, let’s look at the
entertainment domain. Generative AI tools can generate a variety of
synthetic content, such as music, scripts, stories, videos, movies, and video games. They can also translate, localize, and
personalize content. Game development tools such
as Houdini by SideFX leveraged the power of
generative AI to create games, animations, augmented and
virtual reality experiences, and unique characters
with unique behavior. Recently, generative AI powered virtual influencers
and avatars have also gained popularity
that interact with users creating
engaging experiences. Another next big area of impact of generative AI is education. From content generation to personalized and adaptive
learning experiences and simulated
experiential learning, the impact of
generative AI is huge. With their language
capabilities, they can provide
language translations, making content accessible
in different languages. They can create
assignments to provide instant feedback and create
learning journeys and assessment strategies
that support the pace and strengths of
individual learners. Taxonomies can be
generated based on learner performance
and preferences. Generative algorithms can detect special needs and learning disabilities to help learners and educators create
specific lesson plans. Generative algorithms
are also being used to track learner
progress over time. Known as knowledge tracing,
this helps provide the right pacing and content
for individual needs. Tutoring support virtual
and simulated environments and inclusive education
are also benefited. Nolej, for example, offers an AI generated
e-learning within minutes, including interactive videos, glossary, practice questions, and summary for the
targeted topic. Duolingo is a language
learning platform that uses GPT-3 to correct French grammar and create test
items for English. Banking and financial
institutions greatly benefit from generative AI’s capability
to auto detect risks, generate insights, and make financially
literate recommendations. KAI-GPT is the first banking
industry specific LLM that helps banking
applications provide human-like, financially
literate responses. In risk assessment, DataRobot,
for example, can generate synthetic
use cases by simulating potential fraud scenarios
to detect credit risk, fraud risk and
market volatility. For credit scoring,
Personetics and AIO Logic leverage generative
models to detect risks, to determine rates, and
structure custom loans. They automate the assessment of customer credit worthiness and set credit limits
or insurance premiums. An LP powered generative
AI such as Bloomberg-GPT, can analyze news
articles, social media, and other classified
textual data to perform market sentiment
analysis and manage investment portfolios
more effectively. In customer services, generative
AI tools have established conversational systems and assist in financial planning
using robo-advisors, chatbots and virtual assistance. Regulatory compliance
and reporting, financial forecasting
and prediction, portfolio optimization,
anti-money laundering and algorithmic trading are some of the areas that are
greatly benefited. It’s important to
note that some of these applications and tools harness both generative and
discriminative AI models. Now, let’s discuss some of the applications in medicine
and health care research, drug discovery, diagnostics,
and patient care. With their ability to generate synthetic images that
resemble patient data, generative models improve
the robustness of deep learning models used
for medical image analysis. These models can
synthesized data for rare medical
conditions for which very limited data is available. This helps facilitate research, train AI models, and develop new diagnostic tools
for rare cases. In drug discovery, these models help by generating
new molecules and speeding up the drug
discovery process and lowering the
development cost. Telemedicine and
telemonitoring are also benefited from generative AI
driven conversational tools. Rasa, for example,
can establish a medically literate
conversation with patients to provide
immediate medical advice, health-related support and
personalized treatment plans. Other areas in this
domain include electronic health record
or EHR management, health care fraud detection and Medical simulation and training. Let’s look at how
generative AI can empower human resources or HR to
automate repetitive tasks, provide valuable insights
and streamline HR processes. Watson X Orchestrate,
for example, helps automate HR tasks such as creating
job requisitions, screening and short
listing relevant profiles, scheduling interviews,
candidate onboarding and more. Talenteria focuses on
talent acquisition. Leena AI uses
conversational AI systems to automate HR tasks and
employee engagement. Macorva focuses on workplace
and performance management by automatically generating performance documents
and reviews. Other applications of
generative AI and HR include training and development analytics and decision-making, and compliance and
policy implementation. While we have spoken of just
a couple of domains today, generative AI is
ultimately believed to have a significant impact
on all industries. Another big area of
impact is on how we work. As per the
McKinsey report on the economic potential
of generative AI, current generative AI and other technologies have the
potential to automate work activities that absorbs 60-70 percent of
employees time today. Half of today’s work
activities could be automated. 2030-2060. Generative AI is increasing
ability to understand natural language will ultimately impact even
knowledge-based work, typically, associated with higher education and
acquired skills. In this video, you
learned about some of the popular applications of
generative AI across domains. However, it’s
important to note that the potential applications of generative AI are virtually limitless across all industries
in all walks of life. In IT DevOps applications include co-generation
code reviews, automated testing monitoring
and anomaly detection, automated documentation and the continuous integration
and deployment process. In entertainment
generative AI offers exciting possibilities
by generating a variety of synthetic
content and translating, localizing and
personalizing content. In education, Generative
AI transforms the reach and quality of
education effecting learners, educators, and
educational technologies. In finance areas of application
include risk assessment, credit scoring
sentiment analysis, portfolio management,
compliance, forecasting, trading
and customer care. In medicine and health care, we see application
medical research, diagnostics, medicine, medical
training and patient care. In HR applications
are in the areas of talent acquisition,
employee engagement, performance management,
training and development, HR analytics and
policy implementation. In the workplace,
the application of generative AI is
changing how we work, making us more efficient
and successful

Welcome to Tools for Text Generation. After watching this video, you’ll be able to: Describe the basics of
text generation through generative AI. Explain the key capabilities of common
models and tools for text generation. At the core of the text generation
capabilities of generative AI are large language models, or LLMs. Based on patterns and structures learned
during training, LLMs interpret context grammar and semantics to generate coherent
and contextually appropriate text. Drawing statistical
relationships between words and phrases allows LLMs to adapt creative
writing styles for any given context. LLMs are the basis for
many text generation models. Two such examples are generative
pre-trained transformer or GPT and PaLM. These models have evolved into multimodal
models offering multiple capabilities. Let’s learn about the capabilities of
these models through two popular tools, ChatGPT and Bard. ChatGPT is based on GPT as
the large language model and uses advanced natural language processing,
or NLP. While originally ChatGPT only took text
prompts as input to generate new contents, with the newer versions,
it can take both image and text inputs. ChatGPT offers diverse capabilities for
text generation. It is capable of smooth and
context based conversations. Let’s start a conversation with ChatGPT.
To learn a concept, input a prompt that says “I’ve heard about generative
AI and want to learn more.” ChatGPT responds with some basic
information based on the context. When you take the conversation forward
to refine the research by asking, “how can I use generative AI to
improve my storytelling skills?” As a prompt, ChatGPT provides
the response based on the context and question provided by you. Feel free to experiment and
guide the conversation further. ChatGPT will build an informative and
interesting conversational flow. It can also help you with
varied creative tasks. Let’s enter the prompt, “help me to create slides to demonstrate
the features of a learning platform.” And ChatGPT comes with suggestions about
the title, content and visuals for specific slides. Although ChatGPT is most proficient
in English, it can understand and respond to several other languages. Let’s prompt it to write hello
in French and Spanish and it generates the desired output. ChatGPT can also be a useful tool to
assist you in learning a new language or any subject for that matter. Another popular text generation
tool is Google Bard. It is based on Google’s advanced
language models, PaLM, short for Pathways Language Model. PaLM is a combination of transformer
model and Google’s Pathways AI platform. Path AI is based on Pathways, which
are specialized modules responsible for a particular task. Such as NLP or machine translation. In addition to the massive training
data set of text and code, it also pulls information from sources
on the internet to respond to prompts. Try experimenting with different prompts
to explore the capabilities of Bard. Let’s try Bard with a prompt to get a
summary of the latest news on a topic such as “provide a summary of the latest
news on the war in Ukraine.” It provides you with multiple
drafts as the response. You can select one of these or regenerate. Next, let’s try Bard to generate ideas or
solve a problem. Let’s prompt it to provide a strategy for
a digital marketing campaign for promoting a fashion brand. It provides a step by step approach
to the marketing campaign. ChatGPT and Bard offer capabilities for
other valuable use cases. For example, they can help you with
basic mathematics, statistics and problem solving through these subjects. They are also proficient in financial
analysis, investment, research, budgeting and more. Furthermore, ChatGPT and Bard can generate
code and perform code related tasks, across various programming languages and
frameworks. Having interacted with both ChatGPT and
Bard, you’ll notice that ChatGPT is more effective in generating dynamic responses
and maintaining conversational flow. While Bard may be a better choice for
researching the latest news or information on a topic as it has access to web sources
through Google Search and Google Scholar. It’s important to realize that
generative AI models, including GPT and PaLM, are evolving, so their
capabilities and features may change. Apart from Chat, GPT and Bard,
there are other text generators as well. Jasper, for example, generates high quality marketing content
of any length tailored to a brand’s voice. Rytr is a valuable tool for creating
high quality content for blogs, emails, SEO metadata, and ads on social media. Also Copy.ai is great for creating
content for social media marketing and product descriptions. Another tool, Writesonic, offers specific
templates for different types of text, such as articles and
blogs, ads and marketing. There are also tools available for
specific use cases. For example, tools like Resoomer can
generate a summary of a text by extracting key ideas or concepts. Next, tools like Uclassify are used for
classification to assign one or more categories to a snippet of text. Tools for
sentiment analysis understand and generate text that reflects the underlying
emotions expressed in human language. Examples include Brand24 and Repustate. For multilingual language translation,
you can use Language, Weaver and Yandex. It is important to note that many of
the open source generative AI tools collect and review the data shared
with them to improve their systems. This is an important consideration for
interacting with these tools to avoid sharing any confidential or
sensitive information. So, do we have open source
privacy-preserving alternatives? The answer is yes. GPT4ALL, for example, can be installed on
your machines to run as a privacy aware chatbot without Internet or
a graphics processing unit. Further, chatbots like H2O.ai and PrivateGPT are designed to protect user
privacy by running on local machines without any Internet connection
using the power of LLMs. Not only that, you can customize
these tools for use within a specific organization by linking them to your
organization’s documents and databases. Generative AI-based text
generators offer several benefits. These tools are good learning aids as
they provide step by step explanations. They can generate different forms of
text quickly, enabling efficiency for writers and creators. These tools enhance creativity and
inspire new ideas. By enabling engaging and
interactive conversations, they’re useful as virtual assistants and
chat bots. By automating repetitive writing tasks,
they can increase productivity for organizations. With multilingual support, they enable
communication and content localization for global audiences. In this video, you learned that LLMs
interpret context, grammar, and semantics to generate coherent and
contextually appropriate text. LLMs are the basis for
many text generation tools. Two popular text generation tools
are OpenAI’s, ChatGPT and Google Bard. ChatGPT is based on GPT and
Bard is based on PaLM. Both ChatGPT and Bard can generate
different kinds of text, translate languages and answer your questions in
an interactive and informative way. Some of the other tools we talked about
include Jasper, Copy AI, Writesonic. Open source privacy-preserving
text generators include GPT4ALL, H20.ai and PrivateGPT. [MUSIC]

Welcome to tools for
image generation. After watching this video, you’ll be able to describe the basic capabilities of
generative AI models for image generation and explain the key capabilities of common models and tools
for image generation. Generative AI image
generation models can generate new images and customize real
and generated images to give you the desired output. For example, you may
want to generate an image of a child with
a book in her hand. Further, you may want
to change the color of the book cover in
the generated image. Let’s generate a new image using a free AI image
generator, Freepik. You need to enter a text prompt describing the image
you want to create. Let’s say you enter
the following prompt. A boat sailing on a
calm lake at sunset surrounded by lush
greenery and a serene sky. Remember, how you describe your image and the
words you include in the prompt determine
the accuracy and quality of the image
that gets generated. Let’s select the style
and generate the image. Here we have multiple
images generated. You can select and
download an image, or you may want to generate other images by
modifying the prompt. Let’s look at some
more possibilities of image generation models. Image-to-image
translation refers to transforming an image from one domain to another while preserving the original
matter and style. For example, converting
sketches to realistic images, converting satellite
images to maps, converting security
camera images to higher resolution images, and enhancing detail
in medical imaging. Style transfer and fusion
involve extracting the style from one image
and applying it to another, creating hybrid
or fusion images, for example, converting a
painting to a photograph. Inpainting refers
to reconstructing missing or damaged parts of
an image to make it complete. You can use this for art
restoration, forensics, removing unwanted objects in images while preserving
continuity and context and blending
virtual objects into real world scenes
and augmented reality. Outpainting involves extending
the original image by generating new parts to it that are like extensions
of the original. This can be used for
generating larger images, enhancing resolution and
creating panoramic views. The image generation and
modifications capabilities of generative models and tools have evolved with the evolution
of models that power them. OpenAI’s DALL-E is
based on the GPT model. Trained on larger datasets of images and their
textual descriptions, DALL-E can generate high resolution images
in multiple styles, including photorealistic
images and paintings. DALL-E has evolved in the new versions of DALL-E
provide capabilities for generating multiple
image variations and image transformation through
inpainting and outpainting. Stable diffusion is
an open source text to image diffusion model. Diffusion models are
generative models that can create high
resolution images. Stable diffusion is
primarily used to generate images
based on text props, though it can also
be used for image to image translation in
painting and out painting. Invidious StyleGAN
model separates the modeling of image
content and image style, enabling precise
control over style for manipulating specific
features like pose or facial expression. StyleGAN has evolved to generate higher resolution images
with more realistic details. You can explore
generative AI’s text to image generation
capabilities using free tools like Crayon, Freepik, and Picsart. These tools can generate images in different
forms and styles. Fotor and Deep Art Effects
offer a variety of pre trained styles allowing you to create your own
custom styles. DeepArt.io is an online platform that turns photos into
artwork of different styles. Midjourney is a platform that enables image generator
communities that help artists and designers
to create images using AI and explore
each other’s creations. Many generative AI
image generators can also be integrated
as API’s to embed their functionality and capabilities into different
software programs and tools. Some popular image
generators that offer API’s include DALL-E,
Midjourney and Crayon. Technology giants such as
Microsoft and Adobe have also stepped into the world
of AI image generators. Microsoft Bing image creator is based on the DALL-E model. You can access this
tool by navigating to Bing.com/Create or
through Microsoft Edge. This makes Microsoft Edge the first browser with an
integrated AI image generator. Adobe Firefly is a family
of generative AI tools designed to integrate
with Adobe’s Creative Cloud applications, such as Photoshop
and Illustrator. Firefly is trained on
Adobe stock photos, openly licensed content,
and public domain content. Firefly can take text prompts in over 100 languages and include tools that allow you to
manipulate color, tone, lighting composition,
generative fill, text effects,
generative recolor, 3D to image and extend image. In this video, you learned that generative AI-based models and tools can generate new images through both text
and image prompts. They also offer capabilities for image-to-image translation, style transfer, inpainting
or outpainting. A few prominent image generation
models include DALL-E, stable Diffusion and StyleGAN. There are several image generating tools
available that offer diverse capabilities for image generation
and transformation. A few image generators can
also be integrated as API’s. You also learned that Adobe
Firefly is a family of generative AI tools
designed to integrate with Adobe’s Creative
Cloud applications.

Welcome to Tools for Audio
and Video Generation. After watching this video, you’ll be able to describe
how generative AI audio, and video tools create
impactful media content, explain the key capabilities of generative AI audio
and video tools, explore generative AI’s ability to reimagine virtual worlds. Market.us estimates that the
generative AI music market valued at $229
million in 2022 will register a high CAGR of 28.6% to reach $2,660
million by 2032. Generative AI music is created using generative AI
audio capabilities. Over the past few years, these capabilities are helping
companies and individuals, novice or experienced,
simplify their processes to bring their
complicated visions to life. Think about this. Suppose you’ve been
putting off starting your podcast or adding some sound effects
to your remixes. In that case, you’ll love what generative AI audio
tools can do for you. They come in three categories,
speech generation tools, music creation tools, and tools that enhance
audio quality. Speech generation tools are
mostly text to speech or TTS tools that convert
text into audio. While read-aloud
technology is not new, generative AI architecture has upgraded how this
technology works. Deep learning algorithms
are repeatedly trained on vast data sets
of human speech. This allows them to break
down and efficiently replicate vocal characteristics
such as pronunciation, speed, emotion, and intonation. As a result, generative AI, TTS tools create more accurate,
natural sounding speech, which is especially
helpful to those who struggle with
visual impairment, language barriers, and
other reading disabilities. On the fun side, these tools can help you listen to essays, feedback, and notes, which might be easier
than reading them. They can also help you
communicate better. What if you wish to narrate your presentation in
a standout manner? You could log into LOVO, Synthesia, Murf.ai, or Listnr, and choose
from vast libraries of AI voices, languages,
or emotions. You could even create a unique
voice or clone your voice. Some tools will also let
you edit your vocal tracks, pronunciation,
tone, and speed to create a professionally
sounding final product. What about music? Let’s
say one sunny afternoon, the amateur musician in
you is feeling motivated. You could try Meta’s AudioCraft, a generative AI tool, pre-trained on sound effects in 20,000 hours of Meta-owned
or licensed music. There’s also Shutterstock’s
Amper Music, AIVA, Soundful, Google’s Magenta, and the GPT-4-powered WavTool. These tools let you choose
from extensive music banks, different music genres, instrumental styles,
and melodies. All you need to do is
enter a text prompt. Based on your request, the tool will write short
melodies or rifts, suggest or add instruments,
compose a new song, or create a soundtrack for your next YouTube
or Instagram video. Generative AI can also
help you mix, master, and publish your
final musical output on popular streaming platforms. You can even use audio
enhancing tools. These are pre-trained to
identify specific sounds and can add fun sounds to your audio
or remove unwanted ones. For example, Descript can help you remove
background noise, enhance low-quality recordings, and add the desired
sound effects. Audo AI cleans your
files of unwanted noise. Many music generation tools also possess audio editing and
enhancement capabilities. However, some projects need more than eclectic
sound effects. In 2022 Runway AI used generative AI capabilities to produce the Oscar-winning movie, Everything Everywhere
All at Once. Even if you’re not
making big cinema, you can use generative AI video tools in your
everyday life. Let’s say you’re
making a documentary on the lack of
trees in your city. You could log into
Runway’s Gen-1 tool which transforms existing
video clips into different styles or use Runway’s Gen-2 tool to create a video using text
image or video inputs. Alternatively, you can use the EaseUS video tool kit
or the Synthesia app. These tools will allow
you to upload photos. If you don’t have any, use text prompts to generate
the images you need. Additionally, you can use these tools to
record a narration, enhance your audio, convert your video file format,
and publish your video. Synthesia even
allows you to create custom avatars to increase
your brand recall. Generative AI can enhance your
virtual world experience. You can create unique, imaginative virtual worlds with hybrid characteristics
and exotic landscapes. Generative models
can also respond in real-time improving the
accuracy of simulations. Metaverse platforms
employ generative AI to create a more personalized
and engaging user experience. Gaming metaverses
allow you to rapidly generate 3D objects
and even create avatars fitted with
specific personality traits that reflect in
their expressions, behaviors, conversations,
and decisions. The sandbox, for example, is a metaverse where users
can instantly build, own, and market their
games globally. Scenario AI helps create and connect customized
mobile gaming assets. In this video, you learned how generative AI audio and video tools can make an impact. With the simple text prompt, you can produce
human-sounding speech in multiple languages, record songs, add sound effects, or remove unwanted noise, publish professional
videos and animations, build enhanced and
exotic virtual worlds.

[MUSIC] Welcome to Tools for Code Generation. After watching this video, you’ll be able
to describe the basic capabilities of generative AI for code generation, discuss the strengths and limitations of
text-generating tools for code generation, and explain the key capabilities of common
models and tools for code generation. Generative AI model models and Tools for Code Generation can generate code
based on natural language input. Based on deep learning and
natural language processing, or NLP, these models comprehend the context and
produce contextually appropriate code. Code generators can generate a new code
snippet or a program from a text prompt. They can predict lines of code to
complete a partial code snippet. They can also produce optimized
versions of existing code. Further, these code generators can convert
code from one programming language to another. They can also generate summaries and
comments for code to improve documentation. Code generators can also recommend
a complete programming solution. Describe a problem you’re trying to solve,
and code generators will suggest algorithms, data structures, and
the right programming approaches. Let’s explore the capabilities of GPT for
code-generation. OpenAI’s GPT excels in
human-like text generation and demonstrates impressive
capabilities in code creation. Here’s an example of generating
simple Python code through ChatGPT, a tool based on GPT. When you enter a text prompt, “write a Python code to generate
a message to greet a person.” ChatGPT generates the Python code for it. Interestingly, it also provides
guidelines about how to run this code. For generating code, it’s important
that you provide a clear prompt, specify the programming language, and
provide other relevant requirements and constraints. To demonstrate how GPT helps debug code, enter an incorrect code as
a text prompt in ChatGPT. An incorrect code and
text are prompted to chatGPT. ChatGPT provides the correct code and
explanations of the corrections made. GPT also enables translating code from
one programming language to another. It can also generate code documentation
and comments to improve readability. GPT based models and tools have evolved to
generate longer and more accurate codes. This enables using these models and tools
for coding to develop apps, websites, and plugins. Furthermore, the evolution of GPT makes
it possible to generate code from images. For example, you can input the image of
a course outline to generate code for a fully functional app. Google Bard also offers
code generation and debugging capabilities in more
than 20 programming languages. ChatGPT and Bard are valuable tools for
coding for a new programming language, because they provide a step-by-step and
detailed explanation for better understanding. ChatGPT and Bard excel at generating code
with basic logic and programming concepts. They may not, however, be able to generate
large or complex code from scratch. And while these tools understand
programming concepts and syntax, they may not completely
understand semantics. So, while the generated code
is technically accurate, it may still not function as required. It’s important to note that the knowledge
of these models is limited to the data they’re trained on. A specific version of GPT may not be
aware of programming frameworks and libraries released after its training. For example, the knowledge of GPT
3.5 Is limited to September 2021. So if you want a more dated code
generator, consider using models and tools designed specifically for
code generation. GitHub Copilot, an AI code generator, can generate code based on various
programming languages and frameworks. GitHub Copilot is powered by OpenAI Codex,
which is a generative, pre trained language model. It helps developers generate
solution based code. Copilot is trained on
natural language text and source code from publicly available
sources, including GitHub repositories. Copilot can be integrated as
an extension with popular code editors, such as Visual Studio. It can produce code snippets that adhere
to best practices and industry standards. PolyCoder is an open-source
AI code generator. It’s based on GPT when trained
on data from various GitHub repositories written in
12 programming languages. It’s particularly accurate for
writing C codes. PolyCoder offers an extensive library of
predefined templates that can be used as blueprints for code generation for
various use cases. PolyCoder can help create, review, and refine code snippets precisely
customized to requirements. Different code generators offer
specific functionality and features. However, when the requirement is to enable
hybrid cloud developers to write code for diverse needs,
IBM Watson Code Assistant is an option. It’s built on IBM watsonx.ai foundation
models for developers of any skill level. You can integrate Watson Code Assistant
with a code editor. It enables developers to write code
accurately and efficiently with real-time recommendations, autocomplete features,
and code restructuring assistance. Moreover, you can input code or project
files into Watson Code Assistant for analysis. It identifies patterns,
suggests improvements, and generates code snippets or templates. Developers can customize this generated
code for specific project needs. Many other AI driven code generators and code assistant tools are available to help
developers write accurate code faster. Amazon CodeWhisperer can be
integrated with code editors and offers real-time code recommendations. Tabnine can be helpful for
accurate code completions. Replit is a platform that
provides an interactive space for users to code, learn, and collaborate. With automatic code writing and
optimization capabilities, AI based code generators help developers increase
their productivity and quality of code. They enable rapid prototyping
to iterate on design ideas. These tools also help with
cross-platform compatibility and migration by enabling
multilanguage code translation. AI based code generators follow
consistent patterns and coding standards. They can suggest refactoring patterns
to adhere to best practices. However, these tools should be used with
caution to ensure that the AI generated code does not lead to ethical issues. For example, security vulnerabilities,
as these tools may be used for generating malicious data or
data biases based on training. In this video, you learned that
generative AI based models and tools can generate new code from text and
image prompts, optimize existing code, and produce solution based code. ChatGPT and Bard are useful for
simple code generation, debugging, and learning programming. Prominent code generators
like GitHub Copilot, PolyCoder, and IBM Watson Code Assistant
offer varied features such as real-time recommendations, code
restructuring, and solution templates. In general, code generators improve
productivity, accelerate development cycles, promote coding best practices,
and foster consistent coding standards. [MUSIC]