technosip

How I make a simple interactive shell in Golang “command computer keyboard key” by on Hannah Joshua Unsplash So today, I learn something new and a bit of basic thing in Golang. It was making a simple interactive shell. It was just a very simple application, but it kinda looks cool to make it. I’ve been working over 1 year in Golang. So many tools already created by people out there, for example: cobra by , or many more that help us to make command line application. But, somehow I’m curious how to make a simple one without having dependencies with other external libraries. spf13 So just out of curiosity, I start to search how to make it in Google. But what I found is only tutorial using other external dependencies. Everyone is only promoting their own library to ease making an interactive shell 😌. Guys? I just need to make a simple one with the pure Golang package (No external dependencies)😌 But, I found an article about making an interactive shell, like this article: , but, what he does is not like I want. So later with only many examples that I found from websites like Gobyexample.com, etc. http://technosophos.com/2014/07/11/start-an-interactive-shell-from-within-go.html I made a simple Shell application by myself. Here I will explain how I made it below. Shell So before making a simple Shell application, I want to make sure that what I mean shell is the same as what you mean. For me, Shell is an application that will act as the very basic user interface (text-based interface). Some people might say it Command Line Interface (CLI). Making The Application To make the application, for the simplest prototype I will make it like this. $ . main. ls <br> go mod go Read the Command from Terminal To make this, the first thing to do is reading the input. I need to read the input from the terminal. To that, I make it like this. reader := bufio.NewReader(os.Stdin)
cmdString, := reader.ReadString( ) err != nil {
 fmt.Fprintln(os.Stderr, err)
} err '\n' if Execute the Command Then execute the command. After reading the command string from the terminal, now execute the command. cmdString = strings.TrimSuffix(cmdString, )
cmd := exec.Command(commandString)
cmd.Stderr = os.Stderr
cmd.Stdout = os.Stdout
cmd.Run() "\n" Until here, now I can run my simple shell application, but it will only run for one command. After running a command, it will stop. Adding the Infinite Loop To make it receive every command after executed once. We need to add it to an infinite loop. {
 fmt.Print( )
 cmdString, := reader.ReadString( ) err != nil {
  fmt.Fprintln(os.Stderr, err)
 }
cmdString = strings.TrimSuffix(cmdString, )
cmd := exec.Command(cmdString)
 cmd.Stderr = os.Stderr
 cmd.Stdout = os.Stdout
 err = cmd.Run() err != nil {
  fmt.Fprintln(os.Stderr, err)
 }
} for "$ " err '\n' if "\n" if If we run the application it will look like this below simple shell application Dealing with the Arguments Until this line, I’ve finished my first CLI application. But it still doesn’t accept arguments. If I pass an argument, then it will throw an error. $ -lah this wil throw error ls // command To read the arguments, I make it like this below. First I split the command into an array of string. cmdString = strings.TrimSuffix(cmdString, )
arrCommandStr := strings.Fields(cmdString)
cmd := exec.Command(arrCommandStr[ ], arrCommandStr[ :]...) // ... "\n" 0 1 // ... For splitting the text, I use the Fields function from package string. This function is similar to the split string function. If string.Split will split the string by given specific separator. But strings.Fields will separate the words by whitespace. Example: str := arrString := strings.Fields(str)
fmt.Println(arrString) "Hello World    Beautiful World" // [Hello World Beautiful World] Now, my simple shell is already accepting and process the given params. Now, this command should be worked. $ ls -lah
total drwxr-xr-x iman  staff B Nov : .
drwxr-xr-x iman  staff B Nov : ..
-rw-r--r-- iman  staff B Nov : go.mod
-rw-r--r-- iman  staff B Nov : main.go
-rwxr-xr-x iman  staff M Nov : simshel 4280 5 160 6 19 48 6 192 6 11 41 1 38 6 11 43 1 606 6 20 11 1 2.1 6 19 49 Adding Exit Command But, this application is only worked for any built-in application that registered in the environment. Command like exit is not exist, because it is programmed in every CLI application. Then, I make a switch-case handler for each command that doesn’t have a built-in application in the system like the command. exit package main ( )

func main() { := bufio.NewReader(os.Stdin) {
		fmt.Print( )
		cmdString, := reader.ReadString( ) err != nil {
			fmt.Fprintln(os.Stderr, err)
		}
		err = runCommand(cmdString) err != nil {
			fmt.Fprintln(os.Stderr, err)
		}
	}
}
func runCommand(commandStr string) error {
	commandStr = strings.TrimSuffix(commandStr, )
	arrCommandStr := strings.Fields(commandStr) arrCommandStr[ ] { :
		os.Exit( ) }
	cmd := exec.Command(arrCommandStr[ ], arrCommandStr[ :]...)
	cmd.Stderr = os.Stderr
	cmd.Stdout = os.Stdout cmd.Run()
} import "bufio" "fmt" "os" "os/exec" "strings" reader for "$ " err '\n' if if "\n" switch 0 case "exit" 0 // add another case here for custom commands. 0 1 return Adding the Custom Command To add custom commands, I could simply add it to the switch-case handler. For example, I want to add a command like . This command does not exist in any CLI application out there. I want to make it like this: plus $ plus <br> 2 4 5 6 17 To make this, I just need to add a sum function and add the command case handler in the switch-case Conclusion In the end, it’s only a very simple application. But for sure, I learn a few things when making this. And also, when writing this, I found a similar article with this article (here: ). After reading that article, I’m thinking of discarding my draft. But, I’ve written a lot of explanation and example here, so then I decided to post it anyway XD. https://sj14.gitlab.io/post/2018-07-01-go-unix-shell/ Anyway, I’ve put the source code in my Github too here: , if just some chance happens, I will try to add some feature into it later. https://github.com/bxcodec/simpleshell *Indonesian version: * Update: Add another example of custom command https://medium.com/easyread/today-i-learned-belajar-membuat-aplikasi-interactive-shell-sederhana-di-golang-2ef013003393

Today I Learned: Making a Simple Interactive Shell Application in Golang

Co-Designing Complex Systems

In which I get freaked out by a machine learning algorithm that allows Nicolas Cage to star in any movie. On any given day, the number of times I visit Reddit is probably between ten and hundred. I couldn’t give you an exact number, but I enjoy reading new articles that are published within the subreddits and lively discussions that occur as a result. The other day in my usual Reddit-ing spree I came across this article: “ .” Typical absurdist stuff from Reddit, the home of the One True God subreddit dedicated to the (ironic) worship of Nic Cage himself. The comments I found were typical of this, lauding the value of the meme, marveling at the ability for such reconstructions in film to be possible. I must admit, though, after the initial joke of the matter had passed, I was terrified of the implications. It took a lot of scrolling through these comments to find one that matched the same reaction as I had. Nicolas Cage Can Now Be Put into Any Movie in History Thanks to a Machine-Learning Algorithm “Your scientists were so preoccupied with whether or not they could that they didn’t stop to think if they should.” — Ian Malcom, Jurassic Park The primary uses of this are simply put, amazing. Think about the future ability to reconstruct individuals who are no longer alive, and the sense of realism that will give to films. Instead of Daniel Day-Lewis dressed as Abraham Lincoln, film could maintain his brilliant acting but replace his image with that of the real Lincoln. This is great until you think about the potential for evil with such technology. Psychologically we have come to the stand-point that eye-witness testimony is almost always inherently flawed, and instead we rely on visually recorded evidence of an event. Where Photoshop exists this, this can make individual frames difficult to prove legitimate, but video recordings are almost always easier to verify. Think of the power given to individuals or institutions able to edit video recordings. technology Where we have come to understand CCTV as definitive proof of a break-in, what’s to stop the manipulation to portray a political dissident? How do we address someone willing to edit others into potentially compromising videos? These are just a couple of questions that must be considered when we extol the values of any given technology: What potential does this technology have to improve the lives of people and does that outweigh its potential for destruction? I must admit my fears come from a deep-seated pessimism in people doing the right thing. I believe the capacity for individuals to weaponize technologies is almost as endless as the technologies themselves. I also believe there is a sense in modern day technological discussion that all advancement and innovation is naturally good to a society as a whole. Technological objectivism if you will — that realization of individual technological goals will inevitably benefit an advanced civilization. Often the inception of these technologies is based primarily on capital gain with little foresight to social cost I am writing this because I want to confirm that it’s okay to be worried about potentially dangerous uses of technology that are not anticipated. One should not fear being labeled as a luddite for expressing skepticism of technological innovation; in fact, it is a healthy fear that motivates us to regulate potentially dangerous uses of such technology. There’s a great quote from , spoken by scientist Ian Malcom, “your scientists were so preoccupied with whether or not they could that they didn’t stop to think if they should.” Obviously, this discussion isn’t in the context of running from carnivorous dinosaurs but it holds the same weight. Perhaps it’s not just the scientists, but all of us that have a responsibility to stop and think whether we embrace certain technologies and what the implication Jurassic Park should I want to be clear I am not decrying research, innovation, and new technologies; quite the opposite. The rapidity of change over the last century has created better lives for almost everyone in the world, and that’s a really good thing. should always be a primary focus of society and we are all the better for it. Still, it is important to maintain a critical view of impact that many technologies will and do have on society at-large. Science To put this another way: I study Strategy, writ large that’s Military Strategy. The levels of innovation over same time frame as consumer technology has been astounding, and by and large has prevented many deaths. Technology has also had an effect on what we deem an acceptable threshold for engaging in combat. The drone program developed over the last twenty years has lowered that threshold tremendously. Think about it, if you need to place troops in harm’s way, backlash grows as those people suffer casualties. The price of violence is clear to a populace. With advanced technology, the people of a nation do not see that same cost. It becomes easy to commit drones to battlefield with less and less discussion of whether we , and that’s the inherent flaw in such technology. should Technology is the bright neon lettering that hides the ugly shadows in our society. It is the continual promise of ever healthier, stronger, and better community. The veneer of these ever-evolving technologies is usually beautiful enough to look past underlying flaws and unanticipated uses. Not only is it okay to be skeptical of the impact of certain technologies on society, I think it’s the right thing to do. Perhaps you disagree, and maybe all technology really does have a purpose on progressing society, in which case I should probably just sit back and enjoy Nic Cage playing both Jack and Rose in Titanic. Julian is a master’s student at Johns Hopkins School of Advanced International Studies and the Director of Development at arbitror.org Photo “ ”. Originally taken by Project 365 250210 Thanks For The Memory #56: Pete with a CC0. Use of this photo does not indicate an endorsement from its creator.

It’s Okay to be Technoskeptical

In Tomorrow’s Technopia, Will Technology Save Us Or Enslave Us?

Exploring "Clarity Windows" in AI: The Unpredictable Moments of Perceived Consciousness

Εξερευνώντας τα "Clarity Windows" στο AI: Οι απρόβλεπτες στιγμές της αντιληπτής συνείδησης

Explorando las "ventanas de claridad" en la IA: los momentos impredecibles de la conciencia percibida

AI-n "Argitasun-leihoak" arakatzea: hautematen den kontzientziaren une ezustekoak

Exploration des « fenêtres de clarté » en IA : les moments imprévisibles de la conscience perçue

Istraživanje "prozora jasnoće" u umjetnoj inteligenciji: nepredvidivi trenuci percipirane svijesti

AI における「明瞭性の窓」を探る: 知覚された意識の予測不可能な瞬間

Истражување на „Clarity Windows“ во вештачката интелигенција: непредвидливите моменти на перцепирана свест

په AI کې د "وضاحت وینډوز" سپړنه: د پوه شوي شعور غیر متوقع شیبې

AI nisqapi "Clarity Windows" nisqamanta maskay: Conciencia Percibida nisqamanta mana yachay atina pachakuna

Eksplorimi i "Dritareve të qartësisë" në AI: Momentet e paparashikueshme të ndërgjegjes së perceptuar

Истраживање „прозора јасноће“ у АИ: непредвидиви тренуци перципиране свести

AI இல் "தெளிவு விண்டோஸ்" ஆய்வு: உணரப்பட்ட நனவின் கணிக்க முடியாத தருணங்கள்

I Opt Out: System, Governance, And Coordination Must Change Bacause of AI Echoes

The “Why GenAI” problem It is now without question that GenAI is the transformative technology of our times. While the business world is grappling with policy, change management, and “general technophobia,” as this HBR article calls it, I see it as being similar to the early days of the internet. At first, it seemed like a novel technology. Then, people were scratching their heads at its application beyond academia, defense, and other niches, followed by an outpour of startups as infrastructure developed and costs reduced, a dot-com crash, and up to where we are today as a society that runs mostly digitally. All that is to say, GenAI has the footprint of a disruptive trend that will be integrated into all aspects of society. What remains to be seen is the pace at which this integration happens in various parts of our lives. A lack of this understanding is probably why we’re seeing businesses say that they’re unclear about investment priorities when it comes to GenAI: In short, despite realizing the need to increase their investments in AI, too many organizations are slow to embrace the revolution. - BCG | From Potential to Profit with GenAI So, businesses are unsure why they should be using GenAI even though they are interested in the technology.  I think that startups and teams working in this space have the opportunity to educate and evangelize the technology in a way that businesses understand the value directly. Topline and bottom line approach While considering this, I wanted to explore how businesses should be thinking about the technology: As a way to enhance customer value and add new revenue streams (top-line growth)


As a way to enhance operational efficiency and reduce costs (bottom-line growth) I think it's important to think of top-line and bottom-line growth when adopting GenAI. For businesses, it helps to be very clear on what capabilities of the technology they need to hit the specific growth area. Topline GenAI Businesses are often looking to add new lines of revenue by innovating with their products and services. If a business has customers that have these needs, then GenAI is a good fit to solve those needs and add a new source of revenue to them: Customers want to synthesize the information they create on the business’s platform
Customers want to search and find information without having to do mental gymnastics
Customers want to share their knowledge with others on the business’s platform, and they want to be creative and effective in their message.
Customers want to be less involved in doing mundane and routine tasks on the business’s platform. Some examples of topline GenAI integrations are beautifully captured by the folks at Obvious Ventures: Almost all of the products and services above have integrated GenAI to enhance the value they deliver to their customers. In doing so, these businesses seek to increase user engagement and, therefore, have a solid “why GenAI” answer. Bottomline GenAI While growing user engagement and revenue streams are always great, businesses need to sustain topline growth by thinking about their bottom line, i.e., costs. In fact, I think GenAI is most obvious for internal deployment within a business to make operations vastly more efficient and cost-effective. McKinsey agrees: We estimate that gen AI could offer savings opportunities of $1.4 trillion to $2.6 trillion across operations functions, including customer service, R&D, manufacturing, supply chain, and procurement, alongside its impact on the back office.\ Plenty of research has been done on what sectors have the potential to benefit, as shown in the PWC report. However, the use cases aren’t always obvious from reading reports such as these. If you are a developer or a founder, you would want to know more about the value proposition that you can build, which you will not always find in reports like these. So, let’s talk about the use cases briefly…. Any business has these needs where GenAI will be a great fit: Saving time drafting, summarizing, and following up on internal communications (meetings, emails, posts, and chat threads)
Saving time creating documentation, Customer troubleshooting guides, etc., for knowledge management
Assisting employees in their training, learning, and development programs
Synthesizing customer feedback efficiently
Assisting in the creation of impactful marketing assets easily If you focus on these needs and the GenAI capabilities, you should be on the right path to delivering a compelling value-prop to your potential customers in the sectors mentioned above, and possibly even more. GenAI Feature - Business growth fitness summary I’m not going too in-depth with how I rate the problems and how capable GenAI is at solving them, but I think this table is useful for thinking about what problems you’d want to solve for businesses. This will help your team craft a value-proposition that businesses find immediately valuable: GenAI features

How big of a problem is it today?

How well does GenAI natively do it?

Top-line examples

Bottom-line examples



Automated summarization

Low

Very well

Publishers, Content platforms

Business reporting, Meetings, and internal communication, Knowledge Management



Creation

High

Good enough

Marketing businesses, content creation platforms

Documentation, internal communication



Synthesis of information

High

Very well

Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps

Learning and Development, Business Analytics, Research, Feedback processing



Search in natural language

Medium

Very well

Broad applicability

Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation



Automated Actions

High

Additional skills and fine-tuning to be added for each new use-case

Productivity use-cases, payments, home automation, community moderation

Customer support, Meetings, Internal communication, Incident response Takeaway So, if you are thinking about a B2B GenAI idea or are someone looking to adopt GenAI for your own business, I hope this is useful to build a mental model of where GenAI can be leveraged in your case and which specific capabilities deliver the highest ROI for you and your customers. The “Why GenAI” problem It is now without question that GenAI is the transformative technology of our times. While the business world is grappling with policy, change management, and “general technophobia,” as this HBR article calls it, I see it as being similar to the early days of the internet. At first, it seemed like a novel technology. Then, people were scratching their heads at its application beyond academia, defense, and other niches, followed by an outpour of startups as infrastructure developed and costs reduced, a dot-com crash, and up to where we are today as a society that runs mostly digitally. the this HBR article this HBR article All that is to say, GenAI has the footprint of a disruptive trend that will be integrated into all aspects of society. What remains to be seen is the pace at which this integration happens in various parts of our lives. A lack of this understanding is probably why we’re seeing businesses say that they’re unclear about investment priorities when it comes to GenAI: In short, despite realizing the need to increase their investments in AI, too many organizations are slow to embrace the revolution. - BCG | From Potential to Profit with GenAI In short, despite realizing the need to increase their investments in AI, too many organizations are slow to embrace the revolution. - BCG | From Potential to Profit with GenAI In short, despite realizing the need to increase their investments in AI, too many organizations are slow to embrace the revolution. - BCG BCG | So, businesses are unsure why they should be using GenAI even though they are interested in the technology.  I think that startups and teams working in this space have the opportunity to educate and evangelize the technology in a way that businesses understand the value directly. Topline and bottom line approach While considering this, I wanted to explore how businesses should be thinking about the technology: As a way to enhance customer value and add new revenue streams (top-line growth) As a way to enhance operational efficiency and reduce costs (bottom-line growth) As a way to enhance customer value and add new revenue streams (top-line growth) As a way to enhance customer value and add new revenue streams (top-line growth) As a way to enhance operational efficiency and reduce costs (bottom-line growth) As a way to enhance operational efficiency and reduce costs (bottom-line growth) I think it's important to think of top-line and bottom-line growth when adopting GenAI. For businesses, it helps to be very clear on what capabilities of the technology they need to hit the specific growth area. Topline GenAI Businesses are often looking to add new lines of revenue by innovating with their products and services. If a business has customers that have these needs, then GenAI is a good fit to solve those needs and add a new source of revenue to them: Customers want to synthesize the information they create on the business’s platform Customers want to search and find information without having to do mental gymnastics Customers want to share their knowledge with others on the business’s platform, and they want to be creative and effective in their message. Customers want to be less involved in doing mundane and routine tasks on the business’s platform. Customers want to synthesize the information they create on the business’s platform synthesize synthesize Customers want to search and find information without having to do mental gymnastics search search Customers want to share their knowledge with others on the business’s platform, and they want to be creative and effective in their message. creative creative Customers want to be less involved in doing mundane and routine tasks on the business’s platform. routine routine tasks Some examples of topline GenAI integrations are beautifully captured by the folks at Obvious Ventures: Almost all of the products and services above have integrated GenAI to enhance the value they deliver to their customers. In doing so, these businesses seek to increase user engagement and, therefore, have a solid “why GenAI” answer. Bottomline GenAI While growing user engagement and revenue streams are always great, businesses need to sustain topline growth by thinking about their bottom line, i.e., costs. In fact, I think GenAI is most obvious for internal deployment within a business to make operations vastly more efficient and cost-effective. McKinsey agrees: We estimate that gen AI could offer savings opportunities of $1.4 trillion to $2.6 trillion across operations functions, including customer service, R&D, manufacturing, supply chain, and procurement, alongside its impact on the back office.\ We estimate that gen AI could offer savings opportunities of $1.4 trillion to $2.6 trillion across operations functions, including customer service, R&D, manufacturing, supply chain, and procurement, alongside its impact on the back office .\ savings opportunities of $1.4 trillion to $2.6 trillion across operations impact on the back office Plenty of research has been done on what sectors have the potential to benefit, as shown in the PWC report. However, the use cases aren’t always obvious from reading reports such as these. If you are a developer or a founder, you would want to know more about the value proposition that you can build, which you will not always find in reports like these. So, let’s talk about the use cases briefly…. Any business has these needs where GenAI will be a great fit: Saving time drafting, summarizing, and following up on internal communications (meetings, emails, posts, and chat threads) Saving time creating documentation, Customer troubleshooting guides, etc., for knowledge management Assisting employees in their training, learning, and development programs Synthesizing customer feedback efficiently Assisting in the creation of impactful marketing assets easily Saving time drafting, summarizing, and following up on internal communications (meetings, emails, posts, and chat threads) summarizing, Saving time creating documentation, Customer troubleshooting guides, etc., for knowledge management creating Assisting employees in their training, learning, and development programs Assisting Synthesizing customer feedback efficiently Synthesizing Assisting in the creation of impactful marketing assets easily creation If you focus on these needs and the GenAI capabilities, you should be on the right path to delivering a compelling value-prop to your potential customers in the sectors mentioned above, and possibly even more. GenAI Feature - Business growth fitness summary I’m not going too in-depth with how I rate the problems and how capable GenAI is at solving them, but I think this table is useful for thinking about what problems you’d want to solve for businesses. This will help your team craft a value-proposition that businesses find immediately valuable: GenAI features

How big of a problem is it today?

How well does GenAI natively do it?

Top-line examples

Bottom-line examples



Automated summarization

Low

Very well

Publishers, Content platforms

Business reporting, Meetings, and internal communication, Knowledge Management



Creation

High

Good enough

Marketing businesses, content creation platforms

Documentation, internal communication



Synthesis of information

High

Very well

Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps

Learning and Development, Business Analytics, Research, Feedback processing



Search in natural language

Medium

Very well

Broad applicability

Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation



Automated Actions

High

Additional skills and fine-tuning to be added for each new use-case

Productivity use-cases, payments, home automation, community moderation

Customer support, Meetings, Internal communication, Incident response GenAI features

How big of a problem is it today?

How well does GenAI natively do it?

Top-line examples

Bottom-line examples Automated summarization

Low

Very well

Publishers, Content platforms

Business reporting, Meetings, and internal communication, Knowledge Management Creation

High

Good enough

Marketing businesses, content creation platforms

Documentation, internal communication Synthesis of information

High

Very well

Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps

Learning and Development, Business Analytics, Research, Feedback processing Search in natural language

Medium

Very well

Broad applicability

Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation Automated Actions

High

Additional skills and fine-tuning to be added for each new use-case

Productivity use-cases, payments, home automation, community moderation

Customer support, Meetings, Internal communication, Incident response GenAI features How big of a problem is it today? How well does GenAI natively do it? Top-line examples Bottom-line examples GenAI features GenAI features GenAI features How big of a problem is it today? How big of a problem is it today? How big of a problem is it today? How well does GenAI natively do it? How well does GenAI natively do it? How well does GenAI natively do it? Top-line examples Top-line examples Top-line examples Bottom-line examples Bottom-line examples Bottom-line examples Automated summarization Low Very well Publishers, Content platforms Business reporting, Meetings, and internal communication, Knowledge Management Automated summarization Automated summarization Automated summarization Low Low Very well Very well Publishers, Content platforms Publishers, Content platforms Business reporting, Meetings, and internal communication, Knowledge Management Business reporting, Meetings, and internal communication, Knowledge Management Creation High Good enough Marketing businesses, content creation platforms Documentation, internal communication Creation Creation Creation High High Good enough Good enough Marketing businesses, content creation platforms Marketing businesses, content creation platforms Documentation, internal communication Documentation, internal communication Synthesis of information High Very well Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps Learning and Development, Business Analytics, Research, Feedback processing Synthesis of information Synthesis of information Synthesis of information High High Very well Very well Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps Learning and Development, Business Analytics, Research, Feedback processing Learning and Development, Business Analytics, Research, Feedback processing Search in natural language Medium Very well Broad applicability Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation Search in natural language Search in natural language Search in natural language Medium Medium Very well Very well Broad applicability Broad applicability Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation Automated Actions High Additional skills and fine-tuning to be added for each new use-case Productivity use-cases, payments, home automation, community moderation Customer support, Meetings, Internal communication, Incident response Automated Actions Automated Actions Automated Actions High High Additional skills and fine-tuning to be added for each new use-case Additional skills and fine-tuning to be added for each new use-case Productivity use-cases, payments, home automation, community moderation Productivity use-cases, payments, home automation, community moderation Customer support, Meetings, Internal communication, Incident response Customer support, Meetings, Internal communication, Incident response Takeaway So, if you are thinking about a B2B GenAI idea or are someone looking to adopt GenAI for your own business, I hope this is useful to build a mental model of where GenAI can be leveraged in your case and which specific capabilities deliver the highest ROI for you and your customers.

Make Your GenAI Idea Obvious to Businesses

আপনার GenAI আইডিয়াকে ব্যবসার কাছে সুস্পষ্ট করুন

Machen Sie Ihre GenAI-Idee für Unternehmen offensichtlich

Haga que su idea GenAI sea obvia para las empresas

Rendez votre idée GenAI évidente pour les entreprises

अपने GenAI आइडिया को व्यवसायों के लिए स्पष्ट बनाएं

GenAIのアイデアを企業にわかりやすく伝える

귀하의 GenAI 아이디어를 기업에 분명하게 알리십시오

Torne sua ideia GenAI óbvia para as empresas

Сделайте свою идею GenAI очевидной для бизнеса

GenAI Fikrinizi İşletmelere Açık Hale Getirin

Làm cho ý tưởng GenAI của bạn trở nên rõ ràng với doanh nghiệp

让你的 GenAI 理念对企业显而易见

An Overview of Kazakhstan's Fintech Ecosystem

As part of my job search, I'm being introduced to a new language: Python. As the proud owner of a primate brain on the one hand, I'm well aware of the power that , in the context of learning, can have on me. gamification As a technophile on the other, I couldn't miss the emergence of artificial intelligence tools, notably the conversational AI phenomenon that is ChatGPT, developed by OpenAI. How can ChatGPT help me discover Python while making the experience as fun as possible? FROM ChatGPT - the Generalist TO ChatRPG - the Python Expert Although powerful, ChatGPT is even more so when subjected to . So, I've defined how I want ChatGPT to take charge of training me in Python, following a certain formalism to give it as much as possible: role-playing context The role of the AI: ChatGPT will be a Python expert with a strong propensity for teaching. What I expect from the AI: ChatGPT will have to introduce me to the most common syntax and methods of the Python language. The method: in order to achieve this, I want ChatGPT to propose a series of exercises, I'll tell it I have a certain amount of knowledge (JS), and I'll ask it to provide me with external documentation so that I can explore the notions that I am learning further (and, moreover, it will allow me to check that ChatGPT isn't fooling me). The way: finally, I introduce the notion of gamification of my learning, specifying my minimum expectations on this subject: (RPG, Mage, notion of XP). Become a Powerful Mage Initially, ChatGPT provided me with a series of "exercises"; even though it's called that, it's nothing more than code extracts enabling me to grasp the basics of Python syntax. As agreed, he provided me with documentation, gave me experience so that I could level up, and listed the skills acquired during this first phase. Having reached Level 7, I asked ChatGPT to provide me with a more substantial challenge than simply copying code into my IDE. Notice how OpenAI doubts my kindness when I ask ChatGPT to kick some ugly monsters in the teeth. After having been congratulated on my perspicacity facing this first boss, which I easily swept aside, and then provided with a new code-copying "exercise", I had to face my . first real challenges OMG, The Data Dragon! Unimpressed by the enemy, and helped by good , I naturally . documentation slaughtered the hot breathed creature This is where learning via conversational AI makes sense. ChatGPT is capable of providing an while between the two proposals. We'll see a little later that it's capable of being even more complete when it comes to analyzing the code I propose. alternative solution explaining the difference The other advantage is the ease with which you can "shape" the conversation. This way, I was able to obtain an Artifact, and that's . dopamine directly in the veins Yeah I claimed, so what?!? A bit of lore, it's always nice. The Pythonic Monster! After a new series of exercises/lessons, a few questions about instruction 'with', a little challenge on how to throw exceptions, some article reading, and a lot of leveling-up; I asked to take on a . ChatGPT offered me a multi-stage challenge (a succession of small algorithmic problems), with the promise of a chest full of well-deserved loot : new monster Once again, ChatGPT provides a and is complete enough to demonstrate the advantages of its solution over mine. quality correction What's more, I got a really cool amulet! Make ChatRPG Even More RPGed With an MCQ At this stage of my discovery of Python with ChatGPT, I thought it would be a good idea to check that I'd acquired the concepts I'd seen previously. So I asked the AI to generate an MCQ for me, which I included in an encounter with an NPC (non-player character), with the promise of being rewarded with a if I succeeded. familiar So, after answering the questions of a (probably Gandalf undercover), I was able to choose a companion for the rest of my adventure. strange but clever peasant Aware that I sometimes find it difficult to and my code, I've chosen to fight my next battles alongside the Shadowstalker. The beast has a whole arsenal of techniques that will enable me to be more efficient in the future. factor optimize ChatGPT lets me apply a different reading to my code I've identified beforehand! So, I can ask for specific feedback without having to define what kind of feedback I need. according to the needs Here, for example, is the first of the techniques applied to a problem I was given on a quest further along my initiatory journey: the aim of the problem was to generate all the prime numbers up to the number entered by the user. It's got style! Conclusion Considering what I already knew about ChatGPT, the AI has lived up to my expectations regarding its ability to support the discovery of a new programming language. I was, however, very enthusiastic about its ability to bring a gamification layer to the process. Here, in no particular order, is a list of the pros and cons I encountered during my experience: Pros: Quick and easy to set up. Adaptable over time. Satisfactory progress overview. Lore is ok. Ability to correct behavior that doesn't meet your expectations. Possibility of re-launching the latest ChatGPT contribution if it doesn't suit you. "Wow, it can do that ?!" effect. Cons: Commits a few errors and inaccuracies: for example, the first version of the MCQ included a question to which no answer could be given. Requires reminders from time to time: "Don't forget to provide me with documentation", "give out rewards for success", "you must include my progress in the general Lore of our conversation", etc. A few tips if you want to try out this way of learning: Don't use this method on a subject you don't know too much about! If ChatGPT makes a mistake or lies to you, you won't know. Give it as clear and precise information as possible. Don't give it too many instructions at once. At the end of my experiment, I tried to automate the way it generates learning and challenge phases, in a prompt of around thirty lines and with sometimes complex instructions. The result? I broke everything! Nothing beats the expertise of a human being! Pedagogy is a virtue that's reserved for us, the machine is currently unable to appreciate precisely what a student needs to get the most out of what he/she's learning. If you want to be trained in the best possible conditions, call a professional. Bonus I asked ChatGPT to provide me with prompts to generate images using . Stable Diffusion ChatGPT : He's not wrong. What about ? Vast subject. copyright management for artists present in datasets used to train models Thanks!

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Exploring Shadow AI's impact on business: risks, strategies, and the quest for a secure, innovative future. How will companies navigate this new frontier? In December 2023, Amazon unveiled its latest venture, Q, promising a safer alternative to consumer-focused chatbots like ChatGPT. However, the excitement was short-lived. Just three days after the announcement, Amazon Q was embroiled in controversy. Employees were alarmed over its inadequate security and privacy measures, revealing that Q fell short of Amazon's stringent corporate standards. Critics its tendency for "hallucinations" and leaking sensitive information, including AWS data center locations, unreleased product features, and internal discount programs. Amazon's engineers were forced into damage control mode, addressing critical issues tagged as a "sev 2" emergency to prevent future fallout. AI highlighted Around the same time, Samsung Electronics Co. grappled with its own AI-induced headache. Sensitive internal source code found its way onto ChatGPT, spotlighting glaring security vulnerabilities. The response was swift: a company-wide ban on generative AI tools was communicated through an internal memo. Samsung's decision underscored the difficulties of managing data on external AI platforms, such as Google Gemini and Microsoft Copilot, where control over data retrieval and deletion is elusive. This move mirrored the concerns of , who viewed these AI services as a digital . Despite the ban's impact on productivity, Samsung remained firm, opting to develop in-house AI solutions for translation, document summarization, and software development until a secure AI-using environment could be established. 65% of Samsung employees Trojan horse Apple, too, joined the fray, prohibiting its employees from using ChatGPT and similar AI-powered tools. The ban was partly fueled by the tools' affiliations with Microsoft, a direct competitor, stoking fears about Apple's sensitive data security. This trend wasn't exclusive to tech giants; financial behemoths like , Deutsche Bank, Wells Fargo, and others also limited AI chatbots' use, aiming to shield sensitive financial information from third-party eyes. JPMorgan Chase Yet, these restrictions inadvertently birthed a culture of "Shadow AI," where employees use their personal devices at work in their quest for efficiency and time-saving, highlighting a significant policy-practice gap in AI usage. Shadow AI: Unseen Threat Although concrete data is scarce, numerous individuals at companies with AI restrictions have to employing such workarounds — those are only the ones open about it! This Shadow AI usage is prevalent in many organizations, encouraging the use of AI in ways that contradict or violate company policies, thus becoming an activity employees feel compelled to conceal. confessed As I dug into this issue deeper, I found some recent studies confirming that despite about in the workplace, . Recent research by Dell that 91% of respondents have dabbled with generative AI in their lives in some capacity, with another 71% reporting they've specifically used it at work. numerous stories companies restricting genAI use employees don't seem to be using it any less indicates The study conducted by highlights a significant gap between AI adoption in the workplace and the formal policies governing its use in Australia and New Zealand. While 63% of employees in these regions utilize AI for various tasks, only 36% of organizations officially allow it. The survey reveals that AI is being applied for creating written content (51%), enhancing productivity (37%), automating repetitive tasks (37%), improving decision-making (29%), and customer service (20%). However, only 11% of organizations have a comprehensive policy for AI usage, and 21% have no intention to establish any. ISACA Moreover, the study by ISACA indicates a lack of AI-related training within organizations, with only 4% offering it to all staff and 57% not providing any training, even to those directly affected by AI technologies. This situation raises concerns similar to those regarding Shadow IT, where employees use IT resources without formal approval, potentially risking organizational security and governance. Navigating the New Frontier of Risk and Responsibility Much like how Shadow IT snuck up on businesses, Shadow AI is already present, forcing organizations to confront their GenAI stance head-on while still figuring out how to use it. Experts guardrails will not stop employees from using AI tools as they significantly enhance productivity and save time. Therefore, company CIOs must confront this issue and explore mitigation strategies that align with their organization's tolerance for risk. Inevitably, employees with good intentions will utilize these tools to increase their efficiency, so corporate tech leaders can prevent any potential harm to the organization by proactively addressing this trend and managing it effectively. believe Every employee's interaction with AI tools can be a potential point of vulnerability. The history of Shadow IT is marked by significant data breaches, such as the infamous incidents involving , which led to the public exposure of the personal data of 30,000 individuals. These historical precedents serve as a cautionary tale, emphasizing the need for rigorous data governance in the age of AI. unsecured Amazon S3 buckets Shadow AI is a more formidable challenge than Shadow IT for several reasons. First, the decentralized nature of AI tool usage means that the potential for data misuse or leakage is not limited to a technical subset of employees (e.g., developers) but extends across the entire organization. Additionally, inherently learn from the data they process, creating a dual layer of risk: the possibility of accessing sensitive data by AI vendors and the enhanced capability of bad actors to discover and exploit exposed data. AIaaS (AI as a Service) models Strategies to Tackle Shadow AI According to , regional VP of EMEA and Asia and head of digital transformation at SAS, tech leaders with a data-first mindset will be able to drive enhanced efficiencies in 2024 and beyond. This is because maximizing the benefits of generative AI tools is contingent upon well-organized data, necessitating robust data management practices encompassing data access, hygiene, and governance. Amir Sohrabi In his for CIO.com, Nick Brackney, Gen AI & Cloud Evangelist Leader at Dell Technologies, points to "three prescriptive ways" businesses should use to combat Shadow AI successfully. article First, establish a centralized strategy for generative AI usage, involving executive leadership to define use cases, create secure access, and protect data. This approach simplifies enforcement and scaling across the organization while requiring effort to build and identify easy wins to ensure success. Second, keep your data organized and understand which types should not be placed in public or hosted private cloud AI offerings, such as trade secrets and sensitive information. Use AI solutions that allow for complete control or do not retain conversation logs for these types of data. Third, control the AI service by bringing it to your data, whether on-premises or through secure cloud solutions, to leverage advantages in governance, employee productivity, and secure data access. This approach enhances the end-user experience, ensuring compliance and reducing the risk of data exposure. Crafting a clear is crucial for delineating improper AI practices that could potentially harm your organization and for guiding the integration of AI applications in line with data security protocols and risk management strategies. This policy serves as a benchmark, allowing decision-makers to evaluate the usage of AI tools within the organization against established guidelines, swiftly pinpoint any risk exposures, and determine the necessary corrective actions. AI Acceptable Use Policy Ethan Mollick, professor at the Wharton School of the University of Pennsylvania, another thought-provoking approach. He believes that traditional methods for integrating new technologies are ineffective for AI due to their centralized nature and slow pace, making it difficult for IT departments to develop competitive in-house AI models or for consultants to provide specific guidance. The real potential for AI application lies with the employees who are experts in their own jobs, suggesting that for organizations to truly benefit from AI, they must engage their workforce (aka "Secret Cyborgs") in using AI technologies. offers First and foremost, brands should acknowledge that employees at any level could possess valuable AI skills, irrespective of their formal role or past performance. Having detected the among their AI-savvy employees, companies must foster a collective learning environment such as crowd-sourced prompt libraries and create a culture that diminishes the apprehension around AI by offering assurances against job loss due to AI, promoting the use of AI for eliminating mundane tasks and encouraging more engaging work. secret cyborgs Establishing psychological safety is crucial to encouraging open AI usage among employees. Employers should be able to offer substantial rewards for identifying significant opportunities where AI can aid the organization. This could include financial incentives, promotions, or flexible working conditions and be handled through gamification. Today's organizations should act swiftly to determine how productivity gains from AI will be utilized, how work processes should be reorganized in light of AI capabilities, and how to manage potential risks associated with AI use, such as data hallucinations and IP concerns. This calls for a proactive approach to crafting comprehensive AI policies, engaging employees across all levels to leverage their insights, and fostering a culture that rewards AI-driven innovation. As technophiles and businesses navigate the complexities of AI integration, what strategies will you deploy to responsibly and innovatively leverage this transformative technology within your organization or personal projects, ensuring a balanced approach to privacy, security, and efficiency? Don’t forget to check out my previous article to discover ! AI’s dirty secret

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