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Tips for Designers To Tackle Layoffs 

The Layoff Season…

SAP Lab Laid off 300 employees. Ericsson to fire 8500 people. Headlines like these have become very common these days. Over 340 organizations have laid off more than 1.10 lakh people so far across the world. Not only humans but even robots were fired by Google recently. Meta, Amazon, Twitter, Zoom, and Microsoft are some of the major companies to join the layoff bandwagon. These big tech companies have large teams with multiple people with the same skill set. Over the past few weeks, dozens of them have frozen hiring and made significant cutbacks to eliminate redundant positions in departments like HR, marketing, and design. This has led to high competition for jobs in these areas and skill sets and a decrease in job opportunities.

The value of a designer is not always acknowledged, especially in these times of economic uncertainty where cost-cutting becomes a priority. This has also resulted in a cut in the budget for design departments, which leaves designers with fewer employment options. The assumption that design is a luxury rather than a necessity also hinders people from appreciating the worth of designers. This article discusses some tips for designers to tackle layoffs and AI-based solutions that can assist them to stay relevant in UI/UX design.

Automation and technology advancements have led to an increase in the use of design software and tools, making it possible for non-designers to create designs and perform tasks that human designers previously did. This has led to a decrease in the need for human designers in specific industries such as graphic design and website design, where the use of templates and pre-designed elements has become more prevalent.

Who benefits during the recession?

Contract or Freelance Designers

Companies may have less money to spend on design work, which can lead to fewer opportunities for designers. However, designers who are willing to work on a contract or freelance basis may still be able to find jobs, as companies may look to save money by hiring contractors or freelancers rather than full-time employees.

Additionally, designers with a diverse set of skills and the ability to adapt to changing market conditions may be more likely to find work during a recession.

User Experience Designers & Researchers 

UX designers and researchers may still be able to find work, as companies may be looking to improve their online presence and user experience to stay competitive which is why improving their digital products and services may become their key focus area. This can lead to an increase in demand for UX designers and researchers.

Business leaders may also cut costs by streamlining their products and services during these tough times, which can increase demand for user research to understand customer needs and preferences.

It’s crucial to remember that the job market is extremely dynamic and subject to rapid change during a recession, making it challenging to forecast how the demand for designers will change.

Essential Product Companies

Companies that build business-essential products may see an increase in demand, as companies and organizations look to cut costs by investing in more efficient and cost-effective products. These types of products may include items such as software, hardware, and equipment that help companies streamline operations and improve productivity. Additionally, companies that specialize in cost-cutting solutions, such as supply chain optimization or cost-saving consulting, may also see an uptick in business during a recession. It’s important to note that not all product-based companies will benefit during a recession, it will depend on the type of product they produce and the industry they operate in.

How to stay relevant in designing amidst this economic uncertainty?

# Focussing on Soft Skills

While technical expertise is crucial, employers don’t hire only people who possess those skills. The workforce continues to place a high priority on soft skills, also known as employability or transferable skills, which are frequently influenced more by personality than by education or training. The soft skills that may be most important in an uncertain job market for 2023 and beyond, include:

1. Critical thinking skills

Critical thinking involves being able to think creatively and strategically, identify problems, and come up with innovative solutions. These skills are highly transferable across different industries and roles, making them an asset for any team.

2. Communication skills

Effective communication helps in building positive relationships, resolving conflicts, and promoting understanding. Employers value individuals with strong communication skills as they can work effectively with others, and can represent the company positively. It involves both verbal and written communication, and the ability to listen actively.

3. Mental Flexibility

Mental flexibility, also known as cognitive flexibility, is the ability to adapt to new situations, to think outside the box, and consider different perspectives. Employers value individuals with this type of cognitive ability that allows people to adjust their thinking and behavior to changing circumstances. 

4. Teamwork Ability

Teamwork is important in any profession, regardless of the industry or role. Employers value individuals with strong teamwork skills as they can work effectively with others and can contribute to the success of the team and the organization. Teamwork ability is a key skill for achieving common goals, fostering creativity, and promoting a positive work environment.

5. Self Leadership

Employers value individuals with strong self-leadership skills as it involves setting goals, making plans and taking action to achieve those goals, and being self-motivated, self-disciplined, and accountable for one’s own actions.

# Learning AI-based Tools to stay competitive

According to the Global AI Survey, three in four businesses (75%) are either exploring or implementing AI and are increasingly recognizing AI’s potential to transform their operations and create new business opportunities. The survey also revealed that the adoption of AI is still in its early stages, with many businesses facing challenges such as a lack of skilled talent to use these tools, difficulty integrating AI with existing systems, and concerns about data privacy and security.

If there’s one thing that can give designers a competitive edge, it is the use of AI generative tools. AI generative tools are designed to assist designers in creating new designs, patterns, and layouts using machine learning algorithms. These tools can generate a wide range of options based on a set of input parameters, allowing designers to quickly explore different possibilities and find new inspiration.

1. Natural Language Generation Tools (NLG)

NLG tools use algorithms to generate text based on predefined rules or templates. These tools are commonly used for report generation, news article writing, and chatbot interactions. For example GPT-3, Wordsmith, Quill, Articoolo, Textio, etc.

2. Content Ideation Tools

These tools use AI to generate ideas for content topics based on keyword analysis, social media trends, and other data sources. They can help the sales and marketing team identify new topics and angles to explore. For example BuzzSumo, SEMrush, ContentIdeator, Clearscope, etc.

3. Video and Image Generation Tools

There are various AI tools available for image and video generation, which use deep learning algorithms and computer vision techniques to create realistic and engaging visual content—for example Midjourney, DALL-E, Adobe Sensei, Lumen5, etc.

4. Music and Sound Generation Tools

AI can analyze existing music to create new compositions or generate realistic sound effects for video and gaming applications. For example Amper Music, AIVA, Jukedeck, etc.

The Road Ahead:

It’s important to note that while these tools can save time and resources, they also have limitations. They may lack the creativity and nuance that comes with human-generated content, and there is a risk of producing low-quality or biased content if the data inputs or algorithms used are flawed. So a designer who is familiar with these tools will have a better understanding of how to work with them and may be more in demand by companies and clients looking to integrate AI into their design processes. Embrace these opportunities and be open to new ways of thinking.

Disclaimer: I am not an economist. Everything mentioned in this article is supported by extensive research and is not my personal view. I hope this article gives you some clarity and confidence heading into these uncertain economic times. 

About the Author: Unnathi is a UI/UX designer, currently working at Mantra Labs. She is passionate about research and has expertise in building digital systems that provide engaging experiences.

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Machines That Make Up Facts? Stopping AI Hallucinations with Reliable Systems

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

  • Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
  • Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

  • Can AI recognize its own mistakes?
  • What would “self-awareness” look like in AI without consciousness?
  • Are there techniques to make AI second-guess itself?
  • Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live 

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

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