3 Conferences Women In Technology Should Attend To Further Their Career

The month of March offers numerous opportunities to celebrate the progress women are making in the world. This month the U.S. celebrates Women’s History Month and International Women’s Day was March 8th. Despite all of the challenges we’re reading in the media about “Me Too” and diversity in the workplace, there’s also a positive change occurring in the market. Companies across the globe have heightened awareness of the specific challenges women face in the workplace and with breaking into various technology fields. Large technology vendors have created numerous types of diversity and inclusion programs to attract more women into STEM professions. Moreover, these efforts, albeit slowly, are changing the world.

Technology conferences are one area where we see change. Event organizers and technology vendors are creating environments where women can learn from role models on stage, network with peers but also where they can integrate with a wide range of diverse talent. If you’re a woman in tech or inspiring woman in tech, there are three upcoming events where you can take advantage of new networking and development opportunities.

Nvidia’s GPU Technology Conference on March 17-19 Want to learn more about the Artificial Intelligence field? Nvidia’s GPU conference offers multiple opportunities for early career women to accelerate their training in deep learning, and for all women to network and learn from each other. The company has a  Women in AI  program that offers several events specifically tailored to women but also highlights the incredible range of women speaking at GTC. While many companies struggle to find any female speakers, GTC has representatives from industry, academia and their own company. At the event, Nvidia is hosting a Women’s Early Career Accelerator which is a full day of complimentary training for early career women on the fundamentals of computer vision, an opportunity to network with women working in the field, and a free conference pass plus training. The company is also hosting a Women in AI & Deep Learning Breakfast where attendees will learn about the AI ecosystem in the retail and healthcare space. It is the company’s sixth annual Women@GTC event, and I’m personally looking forward to participating in the program. There’s still time to sign up for next week’s GTC event.

Google Next ’19  April 9-11  In April, Google will be hosting the NEXT 19 cloud computing and AI conference. It’s offering a curated diversity and inclusions program with sessions focusing on product inclusion, underrepresentation in technical fields, and inclusive business practices. Industry thought leaders, partners, and customers will provide actionable insights and share personal stories of how they’ve made an impact on inclusivity in their industry. One motivating part of the program is the #IamRemarkable workshops at the event. The workshop goal is to empower women and underrepresented groups to speak openly about their accomplishments in the workplace and beyond. The initiative motivates participants to promote themselves in front of peers and management, thereby breaking gender-related norms and glass ceilings. The Women of Cloud 2.0 session is also sure to be inspiring with a range of role models presenting the latest ideas in cloud computing. Going a step further, Google’s helping parents attend NEXT by offering childcare reimbursement.

Dell Technologies World April 29- May 2 Dell Technologies products span the gamut of IT, so if there are women in tech, they should be at Dell Technologies World. One of the highlights of the program is the Women in Technology luncheon. Last year, I learned about topics such as spotting unconscious biases and how to approach career advancement. Dell also had an impressive group of female employees in significant leadership roles take the stage in 2018. The speakers provided role models in areas such as marketing, channel management, sales CTO and the customer office. I expect this year’s program to offer even more networking opportunities and insights. These are just a few of the events where women can learn more about technology, be inspired and build networks for career advancement. I look forward to seeing you at one of these events.

While the progress has been great, I’d like to leave technology vendors with one word of caution. Diversity programs shouldn’t be a check the box effort. It doesn’t mean providing women (or anyone) a specific role or platform because they fit a demographic category. Diversity means creating a meritocracy with equal pay and equal opportunity for all. Diversity also isn’t limited to gender, race and sexual preference. Diversity and Inclusion programs acknowledge differences in age, religion and political views as well as supporting employees with disabilities. When appropriately executed companies have found that embracing diverse ideas leads to new insight and better business outcomes.

5 Artificial Intelligence Business Lessons From The Field

The big data and analytics market continues to morph as Artificial Intelligence (AI) fields, such as machine learning and deep learning, provide new ways to generate business insights. O’Reilly’Strata Data conferences showcase how AI technologies are changing what companies can do with data across a wide range of industries. Here are four themes from Strata Data that universally apply across various industries and company sizes.

1. Both humans and machines are needed to deliver the best result.

Pinterest’s SVP of Engineering, Li Fan, shared the challenges and opportunities of creating a visual discovery engine. Fan described the challenges with naming an image while also understanding what’s in it and the style behind the image. Correctly defining image attributes is crucial for delivering a successful user experience. For example, a living room has multiple items in it. The company uses computer vision technology to break down the image, understand the objects within the picture and recommend similar things for you to consider.

Since there are 100 billion pins in the database, Pinterest can’t rank every pin for every user in real time. To accomplish this herculean feat, Pinterest uses a graph-based recommendation engine that filters the candidate recommendations for every user. It uses machine models to predict the engagement level of a Pinterest user to a pin and the relevance of pin for a user. It sounds like a simple classification problem, but the system not only has to detect an item, such as a chair, within millions of images. Additionally, the AI system has to understand the style of the object, requiring Pinterest to create feature vectors to help recommend an image based on a user’s style. Where does the human element come in? Data specialists clean, validate and label the data. 

Every night that data is fed into a model and retrained to improve the result. It’s a partnership between Pinterest employees and computer algorithms. Other sessions discussed how people are working with machines to classify data but also to review the results. Takeaway: There’s a role for human plus machine collaboration. In fact, Paul Daugherty,  Accenture’s chief technology & innovation officer, has written a book on this called Human + Machine: Reimagining Work in the Age of AI.

2. It’s not defining the algorithms that stall various AI efforts. 

Both IBM’s Dinesh Nirmal and O’Reilly’s Ben Lorica said preparing data for mathematical models was the primary bottleneck for AI. Nirmal’s keynote focused on operationalizing AI. Nirmal described how real world machine learning reveals assumptions embedded in business processes and in the models themselves that cause expensive and time-consuming misunderstandings. 

Data hygiene has been a critical failure point that has thwarted analytics efforts since the dawn of time. However, it’s an even bigger issue as companies look to incorporate lots of data from various internal and third-party databases. IBM talked about the need for preparing data but also having a structure for AI model management. In a meeting with Ben Lorica, he noted there’s a role within the AI/data science discipline called data engineer that assists in preparing data for the data scientists to use in the algorithm training process. Even in 2018, we’re still trying to eliminate the garbage in yield garbage out problem. 

3. AI will impact jobs in every industry, across all roles.

Michael Chui from McKinsey’s Global Institute for Growth described a world where AI changes every category of employment.  He noted that while AI might not replace a person’s entire job, it will eliminate repetitive tasks within all roles. Minimizing repetitive tasks will enable a company’s employees to focus on higher level functions. 

I agree with this sentiment. However, this outcome will only be positive if individuals, and the companies that employ them, focus on new skills development. It’s unlikely that employees will be self-motivated  to learn new skills during nights and weekends. It will require a dedicated effort from both Human Resources and senior leadership to integrate technology education into an employee’s weekly workload. Retraining the workforce should be a top priority within organizations.

4. Don’t fall into the one tool to rule them all trap.

If you buy a hammer, you want everything to be a nail. Tobias Ternstrom Product Manager, Microsoft described a constant challenge that every technology buyer faces, regardless of industry. Companies want to buy a single tool for the job. Ternstrom described the tyranny of trying to use one software or algorithm for all of your data problems and reminded the group that the best results come from investigating all options and matching the right tools to the right job.

5. Transfer learning can kickstart machine learning efforts in organizations.

In case you haven’t heard of it, Transfer Learning is here, and it’s fantastic. Companies can shortcut the process of developing algorithms by using a model that was trained for a specific task as the starting point for developing a new model for a different job. For example, Ayin Vala talked about how the Foundation For  Precision Medicine used ImageNet’s image recognition algorithms as a starting point for designing an algorithm that discovers Alzehiemer’s traits in MRI imagery. 

Transfer learning is used to speed up training and improve the performance of your deep learning model. Where do you find these models? One place is in the cloud. Vendors such as IBMGoogle and Microsoft are offering pay-as-you-use, pre-trained models as the starting point for computer vision and natural language processing tasks.One thing is clear, while technologists have been on an AI development journey for years, we’ve made tremendous progress in the past five years.