Tag: Large Language Models

To start, Daniel, could you please provide a brief introduction to yourself and your work at Kognic?

 I’m an experienced machine-learning expert and passionate about making AI useful for safety critical applications. As CEO and Co-Founder of Kognic, I lead a team of data scientists, developers and industry experts. The Kognic Platform empowers industries from autonomous vehicles to robotics – Embodied AI as it is called – to accelerate their AI product development and ensure AI systems are trusted and safe.

Prior to founding Kognic, I worked as a Team Lead for Collection & Analysis at Recorded Future, gaining extensive experience in delivering machine learning solutions at a global scale and I’m also a visiting scholar at both MIT and UC Berkeley.

Could you share any real-world examples or scenarios where AI alignment played a critical role in decision-making or Embodied AI system behaviour?

One great example within the automotive industry and the development of autonomous vehicles, starts with a simple question: ‘what is a road?’

The answer can actually vary significantly, depending on where you are in the world, the topography of the area you are in and what kind of driving habits you lean towards. For these factors and much more, aligning and agreeing on what is a road is far easier said than done.

So then, how can an AI product or autonomous vehicle make not only the correct decision but one that aligns with human expectations? To solve this, our platform allows for human feedback to be efficiently captured and used to train the dataset used by the AI model.

Doing so is no easy task, there’s huge amounts of complex data an autonomous vehicle is dealing with, from multi-sensor inputs from a camera, LiDAR, and radar data in large-scale sequences, highlighting not only the importance of alignment but the challenge it poses when dealing with data.

Teaching machines to align with human values and intentions is known to be a complex task. What are some of the key techniques or methodologies you employ at Kognic to tackle this challenge?

Two key areas of focus for us are machine accelerated human feedback and the refinement and fine-tuning of data sets.

First, without human feedback we cannot align AI systems, our dataset management platform and its core annotation engine make it easy and fast for users to express opinions about this data while also enabling easy definition of expectations.

The second key challenge is making sense of the vast swathes of data we require to train AI systems. Our dataset refinement tools help AI product teams to surface both frequent and rare things in their datasets. The best way to make rapid progress in steering an AI product is to focus on that which impacts model performance. In fact, most teams find tons of frames in their dataset that they hadn’t expected with objects they don’t need to worry about – blurry images at distances that do not impact the model. Fine-tuning is essential to gaining leverage on model performance.  

To Know More, Read Full Article @ https://ai-techpark.com/aitech-interview-with-daniel-langkilde/ 

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Chris, could you start by introducing yourself and your role at Zennify and sharing a little about your background in the finance and technology sectors?

I joined Zennify in April 2023 as Chief Executive Officer. I’m a customer success and IT services veteran with over 15 years of experience in the Salesforce ecosystem and 30 years in technology.

Most recently, I was the Senior Vice President of Customer Success at Salesforce. I led the North American Success team responsible for ensuring the retention and growth of the $15B customer base. Before that, I was the COO of Model Metrics (acquired by Salesforce in 2011) and was a board advisor to Silverline and 7Summits, services firms within the Salesforce ecosystem. I was privileged to advise them on scaling and company growth.

We have a fantastic opportunity at Zennify to push boundaries and change the way consulting is done, using AI and tools to accelerate implementations and customer time to value. We strive to be the top boutique Salesforce and nCino consultancy for financial services firms. I’m proud to be here at Zennify and to continue upholding our reputation as one of the go-to partners for financial institutions that want to see accelerated outcomes.

Why financial institutions should ban AI at their own risk:

Chris, you’ve raised the idea that financial institutions should not ban AI at their own risk. Could you elaborate on why you believe AI is crucial for the financial sector’s future and what potential risks they face by not embracing it?

AI has and will continue to impact the breadth, depth, and quality of products and services offered by financial institutions. There are multiple use cases for AI – and a lot of them focus on increased efficiencies. For example, teams can use AI to better predict and assess loan risks, improve fraud detection, provide better and faster customer support through smarter personalization, and analyze data in unstructured ways – all while reducing costs. These are use cases that would have typically taken more time and have more room for errors. Understanding and implementing AI thoughtfully leads to sustainable business growth and staying ahead of your competitors.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-chris-conant-ceo-at-zennify/

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Aurelien, could you elaborate on how your professional experiences and background have contributed to your current position as Product Manager of Esker?

Prior to my current position as Product Manager of Esker’s Customer Service solution suite, I managed Esker technical support teams both in France and the US. I wanted to use the customer knowledge I gathered while helping Esker customers and bring my contribution to providing better products that fully answer customer needs. That led me to becoming a Product Owner within Esker’s R&D department, following the Agile Scrum methodology. I then became a Product Manager for a predictive lead scoring startup where I developed the necessary skills to position and market a new product, aiming at helping marketing and sales professionals develop their businesses.

I then came back to Esker as a Product Manager where I can combine my technical background with my many years of business and technology experience to deliver solutions that relieve Customer Service professionals from time-consuming tasks and enable them to develop new skills.

What is Esker’s overall vision and mission as a company? How does the organization strive to make an impact in the market or industry it serves?

Esker’s mission is to create a better business experience: businesses face uncertainty and need to build stronger relationships with their employees, as well as their customers and suppliers. We want to enable all stakeholders in the ecosystem to generate value together and never come at another’s expense. This is what we call the Positive-sum growth.

With our AI-powered cloud platform, we want to make an impact by automating finance and customer service processes, ensuring team members are more productive and engaged and eventually strengthening the business ecosystems of our customers.

As a Product Manager in the Order Management domain, what are the key challenges you face in delivering a successful SaaS product? How do you address these challenges?

The first challenge that I face is actually not specific to the Order Management domain but rather generic to all product managers: how do you make sure that you identify the most important problems and pains for your users and how do you make sure that you address them and provide value. In a nutshell, you need to remain close to your users and keep this user-centricity when developing your solutions. But I’ll come back to this topic in the following answers.

Then, as our solution targets B2B companies and each company operates slightly differently, another challenge consists in identifying the common needs that can make our product better globally, and not only for a niche of customers. But at the same time, sometimes, we want to provide features that mostly make sense for a given industry (such as pharma, medical device, or building materials), because there is a pain that is not answered by the market and we cannot only rely on the customization capabilities of our consultants to bridge the functionality gap. So, finding the right balance between adding generic and target industry-specific ones is a challenge.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-aurelien-coq/

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The economic downturn is dramatically impacting consumer budgets, making shoppers think twice about their spending. This puts pressure on ecommerce merchants to adapt the online shopping experiences to maximize profitable conversions.

Meeting this challenge requires a focus on five key areas:

Enhance ecommerce site search with dynamic ranking and merchandising:

With every cent counting more, merchants should closely track behavioral data around how products are performing and adjust how high they rank in onsite searches. If a product can’t stand on its own and deliver significant sales, it needs to be less visible. Those less visible products can still be shown to those who might be interested through personalization. Equally, search can rank products based on margin and inventory, so shoppers aren’t shown out of stock items. Modern site search platforms leverage similar technology to ChatGPT, such as large language models and image recognition/deep learning.

Make category pages work harder

Merchants are increasingly focused on driving more traffic directly to category pages, as they try to shorten the path to purchase. With most category pages consisting of rows of product images, the downside is that shoppers that don’t buy will click away without ever seeing the brand messaging and offers that typically exist on a store’s home page.

Merchants can rectify this by displaying more personalized editorial on category pages, highlighting USPs, brand values, discounts, offers, and wider inventory. The aim should be to encourage visitors to explore more of the site, with fewer bounces, more pages-per-visit, and ultimately more sales.

Reward VIP customers

It’s always easiest to generate sales from your most loyal customers—particularly in downturns when buyers become more risk averse. This makes it imperative that merchants segment their returning customers based on customer lifetime value, and invest in delivering custom experiences to repeat purchasers. Reward visiting VIP customers with tailored content and promotions that makes them feel special and valued, including early access to sales, exclusive offers or limited availability products that others can’t get.

Win over socially conscious shoppers

Because budgets have tightened, more shoppers are comparing the costs and benefits of different sites. Brands therefore need to be especially focused on highlighting areas that provide value, such as socially conscious initiatives. These appeal to consumers who are looking to only buy ethically sourced clothing, demand a commitment to sustainability, or only shop organic or locally produced, for example.

Merchants need to be able to recognize shoppers’ social values, and tailor their shopping experience with products or content that appeals. Socially conscious filters can allow shoppers to tailor their experience in line with their values, so they only see organic or sustainable merchandise for example. And socially conscious visual badging, which demonstrates how each purchase benefits a specific cause, such as dollars donated to charity, can really help shoppers feel engaged.

To Know More, Read Full Article @ https://ai-techpark.com/adapting-to-economic-uncertainty/

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