EDITOR’S NOTE
May Greetings as we are deep into one of our hottest summers. With so many tales to share and so many to keep aside, selecting stories was significantly more difficult than narrating them.
Finally, we looked at one of the most topical and game changing areas for our main article for this edition. …. a piece on “Generative AI: Unleashing Creativity and Learning Potential,” which tells about how generative AI has been revolutionizing the learning content space, especially visual learning. Keeping the same theme, we also unpack the story of our client Hindalco, which is now looking at data leverage in revolutionary ways…in a very traditional industry ; “Win Win Game,” our product on negotiation skills is the Product of the month; and, lastly, our “Upgrade Hub,” covers our new look website. Would be great to have feedback from our readers and viewers.
Enjoy !
Generative AI: Fostering Creativity and Learning Capabilities
Gen AI has been revolutionizing so many fields…..content creation in learning and development is no exception. The potential of generative AI to produce new content from writing to visuals, music, and even whole virtual worlds has burgeoned rapidly and promises to grow exponentially. Models such as GPT, DALL-E, and others, are revolutionizing a number of sectors, including education and entertainment, simultaneously challenging our conceptions of the linkages between creativity, visualization, and education.
Comprehending Artificial Intelligence
“Generative AI” is capable of producing new material depending on the data that it has been trained on. In contrast to conventional AI, which mostly examines or categorizes pre-existing data, generative models create fresh content. Large datasets are used to teach these models patterns, which are then used to produce comparable but different content.
The Foundations of Generative AI
Neural networks, especially Transformer-based topologies and Generative Adversarial Networks (GANs), are the foundation of generative artificial intelligence.
GANs comprise of 2 neural networks – the Generator and the Discriminator: one for content generation and the other for authenticity assessment. The Generator attempts to fool the Discriminator, which is tasked with distinguishing between genuine and produced data. Results from this adversarial interaction get more and more realistic.
Transformer-based models, such as GPT, are core to Natural Language Processing, analyse vast amounts of data and produce coherent text or similar sequential data by leveraging machine learning models which comprehend context.
Applications of Generative AI
- Content Creation: Producing text, photos, music, films, and more are all within the realm of generative AI, some more advanced than others today. Deepfake films and AI-generated music compositions are also included in this category. We already have an AI generated symphony. Does it have the personality and quality? Well…. that’s a question for each of us to judge.
- Data Enrichment: Generative AI is utilized in machine learning to produce artificial data that is used to train other models. This improves the robustness of prediction models by addressing problems with limited or skewed datasets.
- Creative Thinking and Design: Architecture, product design, fashion design, and other creative fields are impacted by generative AI. It may be used to suggest changes, come up with fresh design ideas, and even help create artwork.
- Personalization and Suggestions: Generative AI may provide tailored content, such news articles, ads, or product suggestions, by examining user data. In the entertainment and e-commerce industries, among others, this customization improves customer experience.
- Digital Simulators and Virtual Worlds: For training, virtual reality, and gaming, generative AI aids in the creation of virtual worlds and simulations.
Limitations of Generative AI
- Reliability and Quality: Outputs from generative AI may be inconsistent, of low quality, or unreliable. Models may produce inappropriate or inaccurate content, necessitating rigorous quality control and curation.
- Fairness and Bias: The created content may reflect or reinforce biases present in the training data. This is a serious problem for apps that deal with moral or social concerns.
- Legal and Ethical Dangers: Deepfakes, false information, and outputs that break copyright or privacy rules can all be produced using generative AI. This gives rise to moral conundrums as well as legal difficulties with ownership, authenticity, and permission.
- Resources and Level of Computational Demand: Significant computer resources are required for generative AI, particularly for large language models and intricate picture generators. Because of the increased energy usage, this raises expenses and worries about the environment.
- Safety and Security Concerns: It is possible to use generative AI to create viruses, phishing scams, or damaging material. The ability of the technology to produce believable but harmful information increases security issues.
- Absence of Understanding and Context: True comprehension is frequently lacking in generative AI, producing outputs that sound accurate but are devoid of profound knowledge or insight. This can lead to disinformation or outputs that are inappropriate for the intended context.
- Human Supervision and Management: Human supervision is necessary to ensure appropriate usage of generative AI because to its unpredictable nature. However, maintaining control over a large-scale or actual time outputs can be difficult, needing stringent monitoring and ethical norms.
Generative AI for Learning and Development (L&D)
Generative AI has great promise for L&D Both on the content creation and the user experience side.
On the end user side, AI has been making significant contributions. Individualized tutoring based on their progress through the material, adaptive testing where the learner has different difficulty of tests based on their progress, personalised learning and predictive analytics of student performance are typical use cases.
But it is in the content creation side, that Generative AI promises to revolutionalise the space. Scripts, presentations, learning designs, base content, creating Q & As based on existent raw material regurgitated through generative AI models has increasingly been giving better results, with some basic finetuning. Are these fully robust, passing the test of academic rigour and originality…we believe the final human element will be required for the right quality and originality. But a lot of routine content creation can be delegated to the AI engines.
Generative AI for Visual Learning
Visual Learning is a unique space within content creation which we at Knowlens have been pioneering across the country. This involves multiple layers….text, voice, images, videos ..all woven into a seamless integrated story.
Each of the elements are at different stages of advancement.
Text seems to be the most advanced – with translation, script writing engines , Q & As, case studies and other forms of instructional content where AI has been producing reasonable first cut results, albeit with human intervention by the content creator. E.g. GPT-3/4 from Open AI, Llama from Meta, etc.
Text to image has a no of evolving models including DALL-E, Stable Diffusion, Imagen, Midjourney.
Speech has been also making progress with Whisper from Open AI but challenges remain on multiple voices, accents, emotion.
The models are getting better by the day.
While some of the animation videos are seeing significant progress, with enhanced quality dubbing and image creation, videos seem to be some way off a mature product offering, being more complex in its elements.
Bottom line
Massive advances are happening on many fronts but does it really substitute the human creator….as we speak today, we seem to be some way off but many parts of the creative process can certainly be made more efficient and an aid to content creators.
Synopsis
Generative AI is revolutionizing the manner in which we create and learn. It is an effective tool in many sectors, including L&D, due to its capacity to create original material and adapt to different applications. However, proper usage necessitates being aware of its limitations and confronting moral dilemmas. Learning and Development has been no exception with generative AI promising to change the way we look at content creation, including the visual learning space. Time will tell how much and to what extent.
INSIDE KNOWLENS
Hindalco: Cultural Transformation through Data
Hindalco Industries, an Aditya Birla Group firm is a global leader in aluminum and copper production. With its long history in the manufacturing sector, it has over the years developed a strong legacy of process and traditional “experience/knowledge “ based approaches to decision making. In short, a strong traditional legacy mindset pervaded the organization.
But was this approach geared to face the challenges of the 21 st century – with all the advances in technology, science, use of data. Clearly the management at Hindalco had decided that a new approach was needed. A new approach to business … focused on data.
The Commandments
This led to a new set of data principles and practices….the Hindalco version of the 10 commandments ….
The first step of course was defining the commandments:
- It needed to be short, but precise.
- It had to correlate to an approach and reflect in key behaviours of employees
- It needed above all to be eye catching as the key was communication….after all what cultural transformation can happen without the right communication.
Lets take a simple example of a Principle: Data First. Decision Next.
4 words..with a lot of meaning..but very abstract….a meaning open to interpretation.
The Situational Challenge
- The principle needed to be encapsulated in a behavioral situation which captured the essence of the situation
- The situation needed to be universal across the company so that every employee could connect
- The situation needed to be dramatically powerful….remember ..the key was communication
The Knowlens Approach
- The first thing Knowlens did was to understand the key manufacturing processes
- Next , the Knowlens team went about employee interviews, to understand first hand the mental mindset towards digitization and data. This involved a few site visits
- Knowlens abstracted certain situations with universal relevance
- Eventually Knowlens arrived at a short video format approach with a small debrief on this
- To ensure universal acceptance, the film was also dubbed into Hindi
Results
– Hindalco has deployed this across the entire organization with over 63,000 employees and still growing.
Product of the Month
Win- Win Game
This month we introduce the Win-Win game, a course on negotiations skills. So fundamental to sales teams, purchase functions and in general all kinds of deal making, internal and external to organizations.
This module presents a series of different real life negotiating situations through examples in different industries. For instance, we cover institutional sales in the technology industry, channel partner negotiations, financial services negotiations and many more.
Modular in its approach, it allows organizations to choose between a broad based approach to negotiations versus a specialized vertical look at negotiation skills.
Of course key concepts like BATNA, ZOPA are covered but all within the situational context of real life negotiations with actual preparation prior to the meeting and dealing with different kinds of stakeholders during meetings.
Upgrade Hub
The website underwent a redesign last month to better showcase the full Knowlens Product range. Sections dedicated to Learning Management System, Content Studio, Off-the-Shelf Web series-based courses, and our extensive bespoke course creation capabilities are now clearly enunciated.