Skip to main content

AI with great power comes responsibility

[PLACEHOLDER]
Image

Generative AI continues to be front and centre of all topics. Companies continue to make an effort for making sense of the technology, investing in their teams, as well as vendors/providers in order to “crack” those use cases that will give them the advantage in this competitive market, and while we are still in this phase of the “AI revolution” where things are still getting sorted.

 

Photo by Google DeepMind on Unsplash

I bet that Uncle Ben’s advise could go beyond Peter Parker, as many of us can make use of that wisdom due to the many things that are currently happening. AI would not be the exception when using this iconic phrase from one of the best comics out there.

Uncle Ben and Peter Parker - Spiderman

A short list of products out there in the space of generated AI:

Text to image

Video Creation

Text to speech

Copywriting

Conversation

Artistic

NLP/chat

Code


I mean, if you think, like me, that IntelliSense was/is cool, then products such as Copilot and Tabnine, will blow your mind, as you get your own “AI pair programmer” providing you with:

  • Suggestions as you code
  • Writing functions based on the comments you provided
  • Creates unit test for the code you wrote

Allowing you to accelerate the velocity of your development team. According to GitHub research, Copilot is a big success, improving focus by 74%. It also made users feel 88% more productive and efficient by automating repetitive tasks by a staggering 96%.

AI is also a game changer for how we are levering the current search engine providers, and how those tools are, and will continue to evolve because of it (Bing is a good example on how they made AI accessible as part of their search offering). This is because now users can perform their searches asking complex questions. In addition, users are able to receive personalized results, as well as receiving direct, and immediate, feedback. 


AI and its challenges

While experiencing all the great use cases and advantages that Generative AI is providing at one’s fingertips, we are also discovering new challenges (or existing ones that have immediately become more complex). Just to mention a few:

  1. Copyright issues,
  2. Lack of accuracy within the suggestions provided by the algorithm,
  3. Cheating the system/processes,
  4. Students submitting papers generated by AI systems,
  5. Candidates utilizing AI for resumes and interviews,
  6. PII data risks

The lawsuit cases appear to be just the beginning:

  • OpenAI, creators of ChatGPT, receives a lawsuit alleging it stole people’s data (for more go to the business Insider article here )
  • “Microsoft and its computer code-sharing website GitHub, as well as artificial intelligence firm OpenAI, are being sued in California. A proposed class-action lawsuit claims the firms’ AI-powered programming tool Copilot infringes copyright by using millions of lines of human-written code without proper attribution. It is the first big copyright lawsuit over AI and potential damages could exceed $9 billion.”
ai-bias-beolle-1

Conclusion

While this AI “hype” that we are experiencing can be exciting for some, scary for others, challenging for a few, as technology has accelerated faster than expected; the reality is that “Generative AI” is among us.
It will be an important aspect to adopt/tackle/leverage by companies, as well as by individuals, and therefore it needs to be used with the proper ethics and responsibility.

Trending posts

Apple's App Tracking Transparency sealing Meta's fate

Anonymous
If you have been following the recent news on Meta (formerly Facebook) you may have read that Meta recently projected their ad revenue will be cut by a staggering $10 billion in 2022 due to Apple’s new App Tracking Transparency feature (also known as ATT). This has resulted in Meta’s stock to plummet by over 20%. Photo by julien Tromeur on Unsplash - modified by Beolle So what is Apple’s ATT and how does it impact ad revenue? Apple has been releasing multiple privacy features for the last few years. This included Apple’s Mail Privacy Protection and Apple’s App Tracking Transparency feature. You can learn more about Apple’s Mail Privacy Protection in our earlier post by clicking here .  Apple’s App Tracking Transparency (ATT) was launched in iOS 14.5 and iPadOS 14.5 where it prompted users to select if they wanted the app to track their activities across other apps on the device. The prompt is displayed when the user opens an app like Facebook or Instagram for the first time o...
Read more

SLA-SLO-SLI and DevOps metrics

Companies are in need of the metrics that will allow them to stay in business by making sure they meet the expectations of their customers. The name of the game is higher customer satisfaction by winning their trust and loyalty. To do so, you want to provide good products and services. Therefore you need to find ways to monitor performance, drive continuous improvements and deliver the quality expected by the consumer in this highly competitive market. Photos from AlphaTradeZone via Pexel and Spacejoy via Unsplash SLAs, SLOs and SLIs are a good way to achieve the above. They allow clients and vendors to be on the same page when it comes to expected system performance. If we go one level deeper, vendors/providers work on NFRs (Non-Functional Requirements) when working on their solutions. NFRs define the quality attributes of a system. I bring them up because the relationship between them and the SLAs is that they provide, in a way, foundational aspects for the SLA-SLO-SL...
Read more

SRE, DevOps and ITOps

 If you are wondering what the differences between the SRE and DevOps are, as well as how these roles work with ITOps within an organisation then you are not alone; and best of all you are on the right blog post. Often enough business units in a company get confused, assigning the ServiceNow or Jira tickets or any other ticketing system of your preference, to the wrong group, and even having the incorrect expectations when doing resourcing. Let us go through definitions, insights and scenarios that will help you understand the difference. DevOps software development operations - AI Generated When it comes to DevOps and SRE, then you might be wondering which practice came first. While SRE may have originated a bit earlier, internally at Google, DevOps came first publicly as a practice and started to be used by companies. A few years later was when Google decided to open SRE to the world after the publication of the "Site Reliability Engineering" book. Therefore, technically sp...
Read more

Assembling MLOps practice - part 2

 Part I of this series, published in May, discussed the definition of MLOps and outlined the requirements for implementing this practice within an organisation. It also addressed some of the roles necessary within the team to support MLOps. Lego Alike data assembly - Generated with Gemini   This time, we move forward by exploring part of the technical stack that could be an option for implementing MLOps.  Before proceeding, below is a CTA to the first part of the article for reference. Assembling an MLOps Practice - Part 1 ML components are key parts of the ecosystem, supporting the solutions provided to clients. As a result, DevOps and MLOps have become part of the "secret sauce" for success... Take me there Components of your MLOps stack. The MLOps stack optimises the machine learning life-cycle by fostering collaboration across teams, delivering continuous integration and depl...
Read more

This blog uses cookies to improve your browsing experience. Simple analytics might be in place for pageviews purposes. They are harmless and never personally identify you.

Agreed