Leading High Performing Remote Teams
How can leaders ensure that performance remains high in remote or hybrid-work environments?
Content Marketing
In this course, you’ll learn how compelling blogs, videos, podcasts, and other media can reach customers and drive sales. You’ll also learn steps for creating an effective content marketing plan, and some important ways to measure its impact and success.
Content marketing is a essential digital marketing strategy for companies looking to provide relevant and useful information to support your community and attract new customers.
Get started on your content marketing journey today.
Sustainable Innovation in Times of Disruption: Choices for a Better Society
There are opportunities for progress all around us. The key is to innovate on these opportunities sustainably.
To help identify most effective path forward, you'll need to gain a global perspective to these challenges in an open discussion. How can Japan and the world take action to create a more sustainable, innovative world? Where do you fit in?
It's time to find out.
Social Media & Digital Communications: Impact on Global Public Opinion
Social and digital media have dominated the communications industry for decades. But it's no secret that social media has the power to sway public opinion, and the way in which many companies use these platforms could be seen as manipulative.
What do companies need to be aware of when utilizing social and digital media? How can these mediums be used to better communicate strategically with the world?
Discover what top media and communications experts have to say.
CAGE Distance Framework
Want to expand overseas? The CAGE distance framework can help ensure you're constructing a solid global strategy in four areas: cultural, administrative, economic, and geographic. Learn how to leverage useful differences between countries, identify potential obstacles, and achieve global business success.
Servant Leadership
There's more to leadership than driving a team to profit. In fact, there's a word for looking beyond self-interest to prioritize individual growth: servant leadership. Try this course for a quick breakdown of what that is, how it works, and how it can lead to organizational success.
Strategy: Creating Value Inside Your Company
Have you ever wondered why certain companies are more successful than others? The answer is strategy: internal processes that control costs, allocate resources, and create value. This course from GLOBIS Unlimited can give you the tools you need for that strategic edge.
Strategy: Understanding the External Environment
To plan strategy on any level, you need to understand your company's external environment. In fact, your level of understanding can impact hiring, budgeting, marketing, or nearly any other part of the business world. Want to learn how to do all that? This course from GLOBIS Unlimited is the perfect first step!
Using Japanese Values to Thrive in Global Business
Japanese companies have unique cultural, communication, and operational challenges. But they also have values that have led to remarkable longevity. Check out this seminar to hear how these values help earn trust from overseas head offices and develop employees.
Marketing: Reaching Your Target
Every company works hard to get its products into the hands of customers. Are you doing everything you can to compete? In this course, you’ll find a winning formula to turn a product idea into real sales. Follow along through the fundamentals of the marketing mix and see how companies successfully bring products to market.
Basic Accounting: Financial Analysis
Want to compare your performance vs. a competitor? Or evaluate a potential vendor? Then you'll need to conduct a financial analysis. This course will teach you how to use three financial statements and evaluate financial performance in terms of profitability, efficiency, soundness, growth, and overall strength.
Career Anchors
What drives you to be good at your job?
Career anchors are based on your values, desires, motivations, and abilities. They are the immovable parts of your professional self-image that guide you throughout your career journey.
Try this short GLOBIS Unlimited course to identify which of the eight career anchors is yours!
Leadership with Passion through Kokorozashi
The key ingredient to success? Passion.
Finding your kokorozashi will unify your passions and skills to create positive change in society. This GLOBIS Unlimited course will help you develop the values and lifelong goals you need to become a strong, passion-driven leader.
Experience tells us that the main challenge facing startups and large companies involved in AI and machine learning (ML) is slow product development. Scientific models need considerable fine-tuning in terms of functional outputs, and research models require considerable resources and time to be deployed. These problems add up to most AI-ML projects never seeing the light of day.
At a recent GLOBIS seminar, Dr. David Malkin, the director of AI Architecture at Cogent Labs, explained that one of the root causes of failure to develop AI or ML products at all, let alone rapidly, is a conflict in culture between scientists and engineers.
Scientists and engineers play crucial roles in virtually all of the world’s major innovations. Oddly enough, each group thinks very differently, and thus finds it difficult to work with the other. Dr. Malkin says that his company has found a solution: eliminate gaps in culture and promote collaboration.
The Conflict
As Dr. Malkin puts it, “AI scientists view their software as an exoskeleton. AI engineers view their software as a robot.”
The approach of an AI scientist can be summed up in three points:
– Seek accuracy and/or performance match domain
– Create new algorithms and code samples to validate them
– May be embedded in product teams
The approach of an ML engineer can be summed up as:
– Ensure model metrics match product metrics
– Manage code and data inventories
– Track model performances during product lifetime
Scientists prioritize maximizing knowledge through isolated conceptual models that engineers find extremely difficult to quickly convert into real products. From the engineers’ perspective, many other factors, in addition to feasibility and value to clients, must be taken into account. Hence, many companies that implement AI-ML systems have serious difficulties monetizing their AI solutions.
Now that the conflict is clear, the key is eliminating the gap in perspectives between the characteristic cultures of scientists and engineers. This can be done in two ways.
1. Collaborative Approach through Cross-Functional Product Teams
Even with the best AI scientists available, there are serious difficulties in communicating outside of a strictly regulated scientific environment and defining metrics that align with a product’s needs. They tend to build parallel prototypes, recreating production models to iterate and systematically value accuracy over maintainability/scalability.
A culture of collaboration between scientists and engineers, as well the sales team and clients, would benefit any product in the development cycle. Scientists who understand software engineering, equipped with the tools to iterate ideas, would work more closely with engineers and learn the reward that comes with maintainability, stability, complexity, and reduction.
The function of ML engineers is to professionalize design, working towards a viable product from the beginning. These engineers need to understand scientific models and their limitations, then work with researchers in order to understand and improve on new ideas.
There should be a common understanding, from both scientists and engineers, that the model itself is a small part of an AI-ML system.
2. Production for Experimentation
Dr. Malkin summarizes, “[The] traditional split of infrastructure between production, where the infra is well defined, scalable, and ML teams, where the infra is ad hoc, customized, is slowing innovation in AI products.”
– Build your production system to be clone-able for experimentation
– Set up integration testing for model updates
– Build pipelines so that training is part of production
In this way, Cogent Labs has designed a cooperative culture that has allowed it to create a general AI-ML system for the automation of business processes, or “business smartization,” with solutions such as processing manually written, spoken, or even unstructured information. It also involves big data with its Time-Series Forecasting solution, which also incorporates information sources and external networks.
The Takeaway
This AI-ML system is growing to be a general AI system in terms of scalability, providing increasing productivity through the implementation of extended business smartization in AI-driven companies. Something similar occurred in the era before digitalization in terms of the need to transform data into information, except that now the capacity exists to convert this massive, unconnected, unstructured information into automatic decisions – AI-driven, with exponential results.
Further supporting the efficacy of their collaboration concept, in just three years Cogent Labs has managed to launch three products based on AI-ML, acquiring a notable portfolio of clients around the world that includes Nomura, Daiwa Securities, SoftBank, and Canon. The co-creation model sees products developed and perfected together with clients. Cogent Labs also managed to raise JPY 1.472 billion (US$13 million) in capital, which enables the company to continue developing their general AI-ML. It will be exciting to see what new ideas they come up with next.