For tech firms, the risk of not preparing for leadership changes is huge
Tech execs should be more rigorous about succession planning for one important
reason: institutional memory. Tech firms generally are younger than other
companies of a similar size, which partly explains why the median age of S&P
500 companies plunged to 33 years in 2018 from 85 years in 2000, according to
McKinsey & Co. These enterprises clearly have accomplished a lot in their
short lives, but in their haste, most have not captured their history, unlike
their longer-lived peers in other sectors. Less than half of these tech firms,
in fact, have formally recorded their leader’s story for posterity. That puts
them at a disadvantage when, inevitably, they will be required to onboard
newcomers to their C-suites. It’s best to record this history well before the
intense swirl of a leadership transition begins. Crucially, it will help the
incoming and future generations of leadership understand critical aspects of its
track record, the lessons learned, culture and identity. It also explains why
the organization has evolved as it has, what binds people together and what may
trigger resistance based on previous experience. It’s as much about moving
forward as looking back.
The importance of having accountability in AI ethics
In recent years, the EU has made conscious steps towards addressing some of
these issues, laying the groundwork for proper regulation for the technology.
Its most recent proposals revealed plans to classify different AI applications
depending on their risks. Restrictions are set to be introduced on uses of the
technology that are identified as high-risk, with potential fines for
violations. Fines could be up to 6pc of global turnover or €30m, depending on
which is higher. But policing AI systems can be a complicated arena. Joanna J
Bryson is professor of ethics and technology at the Hertie School of Governance
in Berlin, whose research focuses on the impact of technology on human
cooperation as well as AI and ICT governance. She is also a speaker at EmTech
Europe 2021, which is currently taking place in Belfast as well as online.
Bryson holds degrees in psychology and artificial intelligence from the
University of Chicago, the University of Edinburgh and MIT. It was during her
time at MIT in the 90s that she really started to pick up on the ethics around
AI.
Data Platform: Data Ingestion Engine for Data Lake
When we design and build a Data Platform, we always need to evaluate if
automation provides enough value to compensate the team effort and time. Time is
the only resource that we can not scale. We can increase the team, but the
relationship between people and productivity is not direct. Sometimes when a
team is very focused on the automation paradigm, people want to automate
everything, even actions that only require one time or do not provide real
value. ... Usually, this is not an easy decision, and it has to be evaluated by
all the team. In the end, it is an ROI decision. I don't like this concept very
much because it often focuses on economic costs and forgets about people and
teams. Before starting any design and development, we have to analyze if there
are tools available to cover our needs. As software engineers, we often want to
develop our software. But, from a team or product view, we should focus our
efforts on the most valuable components and features. The goal of the Data
Ingestion Engine is to make it easier the data ingestion from the data source
into our Data Platform providing a standard, resilient and automated ingestion
layer.
Beyond OAuth? GNAP for Next Generation Authentication
With GNAP, a client can ask for multiple access tokens in one grant request
(vs. multiple requests). For instance, you could request read privileges on
one resource and read and write privileges on another. ... In GNAP, the
requesting client declares what kinds of interactions it supports. The
authorization server responds to the request with an interaction to be used to
communicate with the resource owner or the resource client. These interactions
are defined in the GNAP spec as first-class objects, which provides extension
points for future communication. Interactions may include redirecting the
browser, opening a deeplink URL in a mobile application or providing a user
code to be used elsewhere. ... GNAP provides a grant identifier if the
authorization server determines a grant can be continued, unlike OAuth2. In
the sample below, the grant identifier, access_token.value, can be presented
to the authorization server if the grant needs to be modified or continued
after the initial request.
The Future Of Work Will Demand These 8 New Skills
Closely related to entrepreneurship is resilience. Humans are nothing if not
adaptable but embracing shifts and bouncing forward (rather than back) will
require new competencies. The skill of resilience requires you to 1) stay
aware of new information 2) make sense of it 3) reinvent, innovate and solve
problems. Finding fresh approaches and flexing based on your insights will be
fundamental to success. ... Inherent to moving forward, is the ability to
believe in a positive future and focus on possibilities. When experts find
fault with a lack of responsiveness, it’s often the result of a lack of
imagination. The skills of being able to envision and foresee what might
happen are critical to staying motivated, inspired and driven to create new
beginnings. ... Success has always been about your network, but achievement in
the future will depend even more on the strength of relationships. Your social
capital and primary, secondary and tertiary relationships will be critical
netting to offer you new learning, access to new opportunities and social
support. The new skill will be the ability to build rapport—and to build it
quickly and it from a distance.
Will Artificial Intelligence Be the End of Web Design & Development
Whilst there has been plenty of hype in recent years around the impact AI will
have to the website design and development community, the reality is that
Artificial (Design) Intelligence technology is still very-much in its infancy
…and there’s a long way to go before we see web designers and developers being
replaced by robots. AI-powered platforms and tools are actually making digital
creatives and engineers more productive and more effective, allowing them to
produce higher-quality, digital experiences at a lower-cost. The concept
behind using Artificial Intelligence to create websites is quite simple:
AI-powered code-completion tools are used to “make” a website on its own and
then machine learning is leveraged to optimize the user interface – entirely
through adaptive intelligence, with minimal human intervention. ... The power
of human creativity brings with it an innate curiosity; we are always looking
to challenge the status-quo and experiment with new forms and aesthetics.
Creativity will always be a human endeavor.
Intelligent ERP: What It Takes To Thrive In A World Of Big Data
While challenging, this requirement led to an innovation that helped the
payment services provider optimize its financial operations and better
understand and expand its business. ZPS collaborated with the University of
Seville in Spain to build a customized cash-flow model to uncover valuable
liquidity and financial planning insights. Within this guarantee-monitoring
model, ZPS uses Intelligent ERP to replicate data on contract accounts
receivable in near-real time to a business warehousing solution and other
reporting applications. An in-memory database then processes the data,
calculates key figures such as customer cash-in and factoring cash-outs, and
uses these figures to determine the amounts to be guaranteed each day.
Furthermore, with a live connection to its business warehousing solution, ZPS
uses a cloud-based analytics solution to let employees access calculated data
and consume reports through intuitive dashboards and predictive stories. By
amplifying the value of its Big Data with Intelligent ERP and augmented
analytics, ZPS allows a larger circle of business users to gain insights into
financial KPIs, such as gross customer cash-ins or days from order.
Is McKinsey wrong about the financial benefits of diversity?
The authors emphasize that this isn’t definitive proof that there is no
connection between racial and ethnic diversity and profits—more research is
needed on that front. They also note several other important caveats,
including that S&P 500 companies are not a random sample of public US
firms, and that their method of identifying race and ethnicity among
executives (using faces and names) is likely to overestimate the number of
white executives. But they criticize McKinsey’s methodology, including its
metric for measuring diversity among executives. They conclude that “caution
is warranted in relying on McKinsey’s findings to support the view that US
publicly traded firms can deliver improved financial performance if they
increase the racial/ethnic diversity of their executives.” Among the
additional research that Green and Hand call for is a way to better examine
whether there is any causal relationship between a firm’s diversity and its
financial performance. McKinsey, by its own admission, is only looking at
correlation.
Data scientists continue to be the sexiest hires around
With the value of data science clear in the potential of these industries,
there is no reason to believe data science will be anything but a growing
profession for years and years to come. AI adoption alone has skyrocketed in
recent years. Now, half of all surveyed organizations say they have applied AI
to fulfill at least one function, with many more intending to invest in
data-driven solutions. As the accessibility and power of data become more
common, so too does the need for data scientists. Now, data scientists must
help businesses navigate a world of global data collection and applications.
From securing business processes to meeting international data security
standards to connecting new and vital patterns in business trends, data
scientists are vital to the success of innumerable businesses across
industries. One such measure they can be part of is setting global data
security standards for various industries. Data science is still one of the
sexiest jobs you can have because it increasingly means helping people and
saving money.
Stanford Researchers Put Deep Learning On A Data Diet
With the cost for deep learning model training on the rise, individual
researchers and small organisations are settling for pre-trained models.
Today, the likes of Google or Microsoft have budgets (read:millions of
dollars) for training state of the art language models. Meanwhile, efforts are
underway to make the whole paradigm of training less daunting for everyone.
Researchers are actively exploring ways to maximise training efficiency to
make models run faster and use less memory. A common practice is to train
small models until they converge and then run a compression technique lightly.
Techniques like parameter pruning have already become popular for reducing
redundancies without sacrificing accuracy. In pruning, redundancies in the
model parameters are explored, and the uncritical yet redundant ones are
removed. Identifying important training data plays a role in online and active
learning. But how much of the data is superfluous? ... For instance, the
capabilities of computer vision systems have improved greatly due to (a)
deeper models with high complexity, (b) increased computational power and (c)
availability of large-scale labeled data.
Quote for the day:
"Successful leadership requires
positive self-regard fused with optimism about a desired outcome." --
Warren Bennis
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