eadwcar karlee varyon
Amit Bolds
LinkedIn and 3rd parties use essential and non-essential cookies to provide, secure, analyze and improve our Services, and to show you relevant ads (including professional and job ads) on and off LinkedIn. Learn more in our Cookie Policy.
In answering this simple question the result was me reviewing my notes like Charlie Day in the infamous Pepe Silvia skit. I realised to properly answer that question I would need to make the value of these AI projects and research papers a little more tangible, and the benefits more measurable and this would require a different format to my recent posts online
Although I try to share one or two generative AI-related projects per week (particularly those that lend themselves to the design studio), I appreciate how overwhelming it can be to monitor and review every machine learning development with the time and attention they deserve given the volume of information published on a weekly basis.
Within my creative circles, most of the interest has been understandably focused on image-generation tools. While text-to-image (T2I) platforms have indeed opened up new ways to explore design, my concern is that most within my peer group are concentrating on one area at the expense of the wider benefits AI can offer. To understand why this space is attracting investment, it's important to appreciate the real value of these paradigm-shifting platforms lies in their underlying technology. Recent advances in neural networks (NNs), large language models (LLMs), natural language processing (NLP), diffusion models, generative adversarial networks (GAN), transformers etc are the driving force behind this T2I revolution, but these technologies are far more sophisticated than image creation alone. This appreciation of the wider ecosystem should help justify the level of enterprise investment, but that extra knowledge will also make it easier for creatives to decide which AI tools/models to include in their own pipeline to primarily enhance the creative output they enjoy, but also automate, simplify and potentially replace the more mundane tasks. Members of my own team have already optimised how they generate pitch decks, tax reports, training syllabi, fielding support, technical writing, blogs, software plugins and even patent templates with AI-powered services, all of which result in more time for creativity.
Although every tech revolution has casualties, in my opinion, these technologies will replace tasks, not people. If you're a novice in a given field (not just design) AI can accelerate you to the point of productivity. If you've already done your '10,000 hours' and possess significant domain expertise and experience in a given field, these tools can make you an order of magnitude more efficient. In my opinion, this is more of a 'rising tides raise all boats' event and less of a job tsunami, and I believe that 'rising tide' will impact the shoreline of every sector that values efficiency.
With two of the biggest players Stability AI & Midjourney dominating the headlines with the release StableDiffusion 2.1 and V4 respectively, all things connected to the generative AI space are showing no signs of slowing. Although I'm a user of both, ignore OpenAI at your peril. With a portfolio of projects including #Dalle2, #GPT3, #WebGPT, #Whisper, the Playground and #GPT4 seemingly just around the corner, despite the competition, OpenAI still currently presents the most compelling, enterprise focussed offering.
Don't take my word for it, Replit & Jasper.AI built directly on top of GPT-3 already boast valuations of around $1B. Although not universally loved GitHub Copilot has fundamentally disrupted the way programmers write code, and Microsoft has already integrated functionality from OpenAI into other solutions under their umbrella such as Bing, Designer and Excel but the Azure OpenAI service introduced last year provides the clearest signal of long-term intent from Microsoft.
I also have to caveat the optimism of this article (and some of my recent posts) by also firmly stating that I don't believe all generative AI applications are created equally. With a rising number of solutions providing tangible value to their users, the advances in AI, particularly NLP, diffusion and LLMs are very real. With that said, I expect the vast majority of projects that emerge in the coming months won't have much impact at all. For every Jasper, Adept or Copilot offering genuine utility, there will be another 20 (some good, some bad, some scams) destined for short-term hype and eventual failure. That's just the nature of any perceived gold rush, when society seeks a quick buck (be that .com, crypto, metaverse or tulips) vaporware will inevitably follow. So I encourage us all to develop some critical thinking toward the space and identify the 'picks and shovels' platforms that can provide you and your organisation demonstrable utility. I also completely understand the deep routed public concerns and scepticism surrounding many of the generative AI platforms that have dominated the conversation. In addition to the genuine ethical debates that require continued discussion, many of us of a certain vintage still suffer from the PTSD inflicted by our first introduction to an 'AI assistant'..... Clippy.
Although many headlines might have you believe otherwise, the emergence of accessible AI models is neither messianic or the herald of disaster, the polarisation we are witnessing is a social phenomenon that occurs during every major technological shift which impacts all of us. Things are never as black and white as they are presented with the truth and clearest minds typically residing somewhere in the grey (or the blue below).
The remainder of this article aims to bring together a collection of seemingly disparate AI projects into a cohesive strategy for a company, and demonstrate the tangible value these tools offer. It would be easy to pick any company and replace a few tasks, but to see the far-reaching impact of generative AI the company selected should be able to feasibly implement the suggested solutions. Therefore the chosen company should possess the appropriate culture, data, distribution model and appetite to adopt this technology into their pipeline. The selected company must also be well-known enough for readers (of all technical levels) to understand how AI can enhance their business and/or staff in their roles. That way we can more accurately examine the impact of generative ai tools, while hopefully making the complexities of Ai more digestible when discussed in the context of a company (or specific job role). My aim in this article is to make it easier for anyone new to the space to see tangible use cases and hopefully demystify some of the jargon.
In July, Netflix named Microsoft as the 'exclusive technology and sales partner' to help power their first ad-supported tier. While my interest back then was little more than a passing one, a lot can happen in a few months and today that partnership looks a lot more interesting. I appreciate the announcement from Netflix refers to their sales partnership, but let's imagine this partnership extends beyond that to include OpenAI (and the wider Microsoft ecosystem). Now when reviewing all the players involved, it feels like a perfect relationship greater than the sum of its parts. One that could see Netflix leverage their platform, IP and an established culture of using big data, with #Microsoft's perfectly positioned technology partnership with #OpenAI (and NVIDIA but more on that later). In this thought experiment, let's examine how the products and research developed by Open AI could be deployed by Netflix to improve customer experience and business efficiency.
Like OpenAI , the Netflix business model is built on a foundation of big data. Netflix monitors every aspect of a subscriber's viewing habits such as when they watch, pause, rewind or fast-forward. The platform tracks everything from your location, your device, and whether or not you leave a show or film before completing it. Combine a built-in rating system and compound across over 200 million subscribers and this translates into a huge amount of high-quality data.
With this information, Netflix knows what content to produce (and what to cancel) which results in more satisfied customers and better capital allocation. The most notable example of these unique data-driven insights in practice was House of Cards.
Netflix identified that the British version of House of Cards was attracting a large audience of subscribers. Those members who watched the British version of House of Cards also seemed to favour movies starring Kevin Spacey. Identifying these signals in the noise led to Kevin Spacey being cast in the lead role of the modern reboot. That same data was also instrumental in how most of the characters were cast, the script was developed and how the overall narrative progressed. The show was a massive hit.
In the age of TikTok, the idea of high-end content designed, built or curated by the algorithms might seem standard, but back in 2011 this was revolutionary. Cable networks couldn't dream of having the insight Netflix were able to gather on their viewers and the streaming giant would go on to leverage data to its advantage across the entire business.
With Netflix's innovative culture and credentials of implementing machine learning established, generative AI feels like a natural progression. Let's look at a few areas where they might implement some of the technology.
The underlying science and development behind how Netflix creates artwork deserves an article of its own such is the complexity. Here is an overview of how it works today, why it is a critical part of the company strategy, and why it is challenging to maintain.
Explaining why artwork is important to Netflix is the easy bit. Just imagine the service without it. In the absence of artwork, the platform becomes the soulless vacuum of nothingness below and very unlikely that one program would catch a subscriber's attention over another.
90f70e40cf