The Darius Gant Show Darius Gant

In this episode we speak with Wendy Gonzalez, CEO of Sama, a company at the forefront of providing high-quality training data for AI technologies, used by major companies like Walmart, Google, Nvidia, GM, and more. Under Wendy's leadership, Sama has gained recognition for its rapid growth and her dedication to creating employment opportunities in underserved communities. The conversation delves into Sama's foundation on the belief of distributing opportunity through job creation in the digital economy, their focus on AI data pipeline development, and the shift towards data annotation and AI model training. Wendy discusses the importance of human judgment in AI development and Sama's approach to employment, and fostering a diverse and skilled workforce. She also touches on Sama's involvement with synthetic data, the ethical considerations in AI, the potential of generative AI in various applications, and how Sama addresses the challenges and opportunities in AI technology development while emphasizing social responsibility and workforce development in underrepresented communities.
 
If your company is looking to scale its AI initiatives, head over to Tesoro AI (www.tesoroai.com). We are experts in AI strategy, staff augmentation, and AI product development. 
Founder Bio:
Wendy Gonzalez is the CEO of Sama, which is a leader in providing high-quality training data to power AI technology and is used by leading technology companies such as Walmart, Google, NVIDIA, GM, and Getty. Under Wendy’s leadership, Sama has placed on the Inc. 5000 list as one of America's fastest-growing private companies for the past four years. She has also been recognized by the Globee Awards for Women in Business, the TITAN Awards for Women in Business, and the Stevie Awards for Women in Business due to her leadership and Sama’s growth.
 
Prior to taking on the role of CEO, Wendy spent five years at Sama as COO, and is an active Board Member of the Leila Janah Foundation. As CEO, she is one of few female leaders within the male-dominated AI industry. With two decades of managerial and technology leadership experience, Wendy is an executive passionate about building high-performing, high-functioning teams that develop and scale innovative, impactful technology.
 
Time Stamps:
 
02:14 Wendy's Journey: from management consulting to AI leadership
04:03 Bridging talent and opportunity in AI
06:42 From data annotation to AI model validation
08:24 Sama's approach to AI data
13:05 How Sama works with businesses
14:27 Generative AI vs traditional ML
21:08 Sama's Role in the future of AI
23:38  Monetization and investment in AI technologies
25:49 The importance of high accuracy in AI applications
28:01 Addressing multilingual support and complex data categories
29:52 Data privacy and security in AI development
36:06 The role of synthetic data in enhancing AI models
37:58 Empowering underrepresented communities through AI jobs
43:37 Excitement and challenges in AI for 2024
 
Resources
Company website: https://www.sama.com/
Twitter: https://twitter.com/SamaAI
Instagram: https://www.instagram.com/sama_ai_/
LinkedIn: https://www.linkedin.com/company/sama-ai/

Data quality issues can arise at various stages of the data pipeline, from data ingestion to model deployment. Common issues include null values, schema drift, and incorrect calculations. These seemingly small issues can have a significant impact on the accuracy and reliability of the data, leading to broken dashboards and loss of trust in the data system.
In this episode, host Darius Gant interviews Abe Gong, the founder and CEO of Great Expectations, a leading data quality tool. Abe shares his insights into the world of data quality and how Great Expectations is solving the systemic problem of data quality in organizations. He explains the importance of building a robust testing system for data, similar to what software engineers do, in order to ensure accurate and reliable data. Abe discusses common data quality issues and how Great Expectations helps teams identify and fix these issues. He also explores the intersection of data quality and AI, highlighting the role of GX in ensuring the accuracy and trustworthiness of AI models. Throughout the conversation, Abe emphasizes the need for collaboration and communication in data teams to build trust and achieve data-driven success.
If your company is looking to scale its AI initiatives, head over to Tesoro AI (www.tesoroai.com). We are experts in AI strategy, staff augmentation, and AI product development. 
Founder Bio:
Abe Gong is a founder and CEO at Great Expectations, the world’s leading open source tool for data quality. Prior to working on Great Expectations, Abe was Chief Data Officer at Aspire Health, the founding member of the Jawbone data science team, and lead data scientist at Massive Health. Abe has been leading teams using data and technology to solve problems in health, tech, and public policy for over a decade. He speaks and writes regularly on data, AI and entrepreneurship.
 
Time Stamps:
01:58 Abe Gong’s background and experience in data science
03:45 The pain point in the market that led to the creation of great expectations
05:00 Common errors and issues in data quality
06:47 Identifying and solving data quality issues
09:43 How great expectations support companies deploying AI models
12:45 Great Expectations involvement in generative AI use cases
16:34 Understanding the sensibilities and workflows of data developers
19:42 Building a remote-first team with a focus on open-source collaboration
22:11 Tips for running a remote team efficiently and effectively
24:41 Hiring independent and action-oriented individuals for remote work
27:24 Raising founds journey for Great Expectations.
30:08 Importance of technical leads on data teams
32:52 Difference between enterprise software sales and open source models
34:06 What is coming up for Great Expectations in the 2024
 
Resources
Company website: https://greatexpectations.io/
Twitter: https://twitter.com/expectgreatdata
LinkedIn: https://www.linkedin.com/company/greatexpectations-data/

One of the primary benefits of AI in cybersecurity is its ability to enhance perception and reasoning. Traditional security measures often rely on manual analysis of logs and data, which can be time-consuming and prone to human error. AI, on the other hand, can analyze vast amounts of data in real-time, detecting patterns and anomalies that may indicate malicious activity. This improved perception allows for early detection of threats and faster response times.
 
In this episode, we speak with Gregor Stewart, an executive at Sentinel One, about the intersection of AI and cybersecurity. Gregor shares his journey in the tech industry, from his early fascination with text adventure games to his academic studies in AI and cognitive science. He discusses the evolution of natural language processing (NLP) and how AI has transformed the field of cybersecurity. He also explains how Sentinel One uses AI to detect and mitigate malicious activity on endpoints and in cloud environments emphasizing the importance of automation and autonomy in cybersecurity and how AI can enhance the speed and accuracy of threat detection and response.
 
If your company is looking to scale its AI initiatives, head over to Tesoro AI (www.tesoroai.com). We are experts in AI strategy, staff augmentation, and AI product development. 
 
Founder Bio:
 
Gregor Stewart is Tech executive with over 20 years of experience in software development (engineering and product management) at both privately and publicly held companies. Deep domain expertise in delivering Data Science, Machine learning and AI applications, with a particular focus on Natural Language technologies, Generative AI models (including fine-tuning, cost optimization) and multi-modal, conversational problems like customer journey modeling. Led and scaled engineering and research teams across the world through IPO (NYSE:MDLA) and private equity aquisition (Thoma Bravo), both organically and by acquisition; Adept at building and scaling large, high-performing distributed teams that successfully deliver products to enterprise and mid-market customers.
 
Time Stamps:
 
02:20 Introduction and background of Gregor Stewart
10:12 Balancing expectations as a person guiding AI development
12:37 Executive challenge of prioritizing and budgeting AI projects
14:05 Overview of Sentinel One’s core products and implementation of AI.
16:46 The complexity of maintaining a good security posture
19:58 The skill level required for resolving security issues with AI assistance
21:53 AI’s ability to detect and resolve attacks that non-AI systems can’t.
28:09 How AI allows for more efficient querying of security data.
31:37 Transitioning from a startup to a larger organization with centralizing AI expertise.
36:18 Using AI to distribute and answer information
38:13 What is coming up for Sentinel One in the 2024
 
Resources

Company website: https://www.sentinelone.com/
Instagram: https://www.instagram.com/sentinelsec/?hl=es
Twitter: https://twitter.com/SentinelOne
LinkedIn: https://www.linkedin.com/company/sentinelone/

In a world driven by data, organizations are constantly seeking ways to collaborate and leverage the power of artificial intelligence (AI) to solve complex problems. However, the challenge lies in the fact that data collaboration often requires sharing sensitive information, which raises concerns around privacy, security, and intellectual property (IP) protection.
 
In this episode we interview Alon Kaufman, the CEO of Duality Technologies, about the challenges and solutions surrounding data collaboration in the age of AI. Alon shares his journey in the field of AI, starting from his early days in computational neuroscience to his experience at RSA and ZoomInfo. He explains the problem of data collaboration and the need for secure methods to share data while maintaining privacy and security.
 
Alon introduces Duality Technologies and its mission to unlock the potential of data collaboration through encryption and privacy-enhancing technologies. He discusses the unique approach of using mathematics and encryption to enable secure data collaboration, highlighting the benefits for industries such as healthcare, finance, and government.
 
If your company is looking to scale its AI initiatives, head over to Tesoro AI (www.tesoroai.com). We are experts in AI strategy, staff augmentation, and AI product development.
 
Founder Bio:
 
Alon Kaufman, Co-Founder and CEO of Duality Technologies, has 20 years of experience in the hi-tech arena, commercializing data-science technologies, leading industrial research and corporate innovation teams. Prior to founding Duality he served as RSA’s global director of Data Science, Research and Innovation. In addition to his leadership experience, he is accomplished in the fields of artificial intelligence, machine learning and how they interplay with security and privacy, with over 30 approved US patents in these fields. He holds a PhD. in Computational Neuroscience and machine learning from the Hebrew University and an MBA from Tel Aviv University.
 
Time Stamps:
 
01:40 Alon’s background and transition into AI
04:52 Alon’s experience in data science and problem-solving
07:37 The challenge of data collaboration within AI
10:22 Duality as a software solution for data collaboration
13:34 Encrypted collaboration without sharing data
17:54 AI’s role in accessing unbiased data
20:33 Using duality to access partner’s data
23:50 Duality as a try-before-buy platform for startups
26:25 Government collaboration for cross-border use cases
29:06 The technical talent required in the early phases of building Duality
32:06 Building a global team and leveraging expertise in different locations
34:32 Balancing remote work and in-office collaboration for productivity
38:46 The funding journey and the benefits of being part of the AI ecosystem.
40:04 What is coming up for Duality in the 2024
 
Resources:
 
Company website: https://dualitytech.com/
Twitter: https://twitter.com/DualityTech
LinkedIn: https://www.linkedin.com/company/duality-technologies/
 

Generative AI, particularly retrieval augmented generation (RAG), is in high demand as companies seek to leverage its capabilities to improve operational efficiency and enhance customer experiences. - Building a successful generative AI project requires a combination of engineering and research skills, as well as a deep understanding of the specific use case and data sources involved. - Ongoing maintenance and monitoring are crucial for ensuring the continued success of generative AI projects, as models need to be regularly updated and evaluated to maintain accuracy and relevance.
 
Today we sit with Bartek Roszak, who is the head of AI at STXNext, about the integration of ethical AI practices and compliance. Bartek shares his journey in AI, starting as a stock trader and transitioning to data science and deep learning. He discusses the evolution of STXNext from a Python development house to an AI-powered company and the growing demand for generative AI solutions. Bartek explains the concept of retrieval augmented generation (RAG) and its applications in various industries. He also highlights the importance of prompt engineering and the challenges of maintaining AI models post-deployment.
If your company is looking to scale its AI initiatives, head over to Tesoro AI (www.tesoroai.com). We are experts in AI strategy, staff augmentation, and AI product development.
Founder Bio:
Bartek Roszak is a luminary in the AI sphere and the driving force behind STXNext’s AI strategy. With significant roles and achievements in the field of AI, Bartek has a background in stock trading and transitioned to data science and deep learning. He has experience in building AI solutions for various industries and specializes in generative AI, particularly in the area of retrieval augmented generation (RAG). Bartek is passionate about integrating AI into business operations and helping companies leverage AI for competitive advantage.
Time Stamps:
02:26 Bartek’s background and transition into AI
04:51 Evolution of STXnext into an AI-focused company
07:12 Popular AI use cases and interest in generative AI
10:07 What is retrieval augmented generation (RAG): Use cases and interest
12:51 External uses of generative AI with high demand
14:48 Skills required to build and deploy gen AI products
16:38 Minimum experience with LLMs or machine learning needed to work with Gen AI
19:49 The importance of quality assurance in machine learning projects
21:43 Importance of having skillsets in prompt engineering.
23:19 Diversifying LLM base to mitigate downtime risks
24:52 Creating a competitive advantage by using LLM as part of a larger system
28:04 The process of working with clients on AI use cases
32:51 Post-project maintenance and client involvement
34:23 Transitioning the project to the client’s data science team
36:40 STX Next’s plans for 2024
Resources
Company website: https://www.stxnext.com/
Twitter: https://twitter.com/STXNext
LinkedIn: https://www.linkedin.com/company/stx-next-ai-solutions/
Instagram: https://www.instagram.com/stx_next/

Generative AI has become a game-changer in various industries, with applications ranging from content generation to market research. - The demand for generative AI talent is high, and it is an employee’s market in this field. - Prompt engineering is crucial in effectively communicating with language models and ensuring accurate and human-like outputs.
Building Gen AI products requires a different skill set compared to traditional software development. It’s important to understand prompt engineering, have knowledge of neural networks and backpropagation, and be familiar with frameworks like Langchain and vector databases.
In this episode, we interview Ehmad Zubair, the co-founder of a web and mobile development agency that has recently shifted its focus to generative AI. Ehmad shares his journey from being a software engineer to diving headfirst into the world of AI. He discusses the challenges of building real-world AI applications and the skill sets required for success in the field. Ehmad also highlights the exciting use cases his company is working on, including AI-powered content generation and market research tools. Don’t miss this insightful conversation about the booming world of Gen AI.
If your company is looking to scale its AI initiatives, head over to Tesoro AI (www.tesoroai.com). We are experts in AI strategy, staff augmentation, and AI product development. 
 
Founder Bio:
 
Ehmad Zubair is the visionary CEO of Cogent Labs, a cutting-edge software development agency he founded in 2021. With a robust foundation in software engineering since 2014, Ehmad has steered Cogent Labs from its inception to a dynamic team of 52 professionals in just three years. Under his leadership, the company has carved out a niche in Generative AI, alongside web and mobile development, contributing to global impact projects, including collaborations with UNICEF's Internet of Good Things.
Ehmad is not just a business leader but also an influential voice in the tech community, running a successful YouTube channel. Here, he shares insights into the software engineering landscape in Pakistan, offering valuable knowledge to aspiring engineers and industry veterans alike.
Outside of his professional pursuits, Ehmad is an avid football enthusiast and a content creator, further showcasing his diverse interests and talents. Follow his journey and insights on his website, cogentlabs.co, on Instagram @ehmad.zubair, and on his YouTube channel, Ehmad Zubair, to stay at the forefront of software engineering and Generative AI innovations.
 
Time Stamps: 
 
02:09 Introduction and background of Ehmad Zubair
04:06 The rise of online programs like Udacity for AI upskilling
07:46 Entry into building real-world AI applications
09:48 Use cases and applications of generative AI
14:48  Challenges of selling Gen AI to enterprise customers due to data privacy and security concerns
18:05 Challenges of working with cutting-edge generative AI models
21:04 Importance of hiring curious and adaptable team members
22:00 Required skills for building generative AI products
25:56 Prompt engineering as a programming language for generative AI
27:58 Code is still the most efficient way to program machines.
29:41 Finding the right talent for Gen AI 
32:28 Building tools to save time and money is the focus.
34:54 Upcoming Projects for this 2024.
37:30 Introduction to a fact-checking AI agent that helps minimize hallucinations and hyperboles
40:11 How to get in contact with the Cogen Labs team
 
Resources
 
Company website: https://cogentlabs.co/
Facebook: https://www.facebook.com/cogent.labs.co
LinkedIn:https://www.linkedin.com/company/cogentlabs/
Instagram: https://www.instagram.com/cogent.labs.co/