Rohit
Talwar: “A great futurist really helps people
get insights into the emerging future”
Marie-Hélène
Caillol:
"With internet and
globalisation, we are de facto embarked on the same boat from Paris
to Beijing, Johannesburg and Rio. We see today that we can't trust the
system invented 70 years ago to produce peace any longer. It is high
time to create new tools empowering stakeholders of the planet (citizens),
stakeholders of the future (young leaders) ... "
David
Shrier:
"What is the metaverse?
Quite simply, it’s a digital platform to help people collaborate,
work and play in new ways, in an immersive 3D environment."
Lead Article:
Generating
Democracy: AI and the coming revolution in political communications
by Alice Dawson and James
Ball
Published by Demos January 2024
Alice Dawson
James Ball
ACKNOWLEDGEMENTS
We would first like to thank Cavendish for their generous support and
enthusiasm for this
project.
Thank you to the people who took part in our focus groups for candidly
sharing their views
and perspectives on the use of AI in communications with us. All contributions
were invaluable
in shaping our thinking for this project.
Thank you also to our workshop participants and the experts we interviewed
from across
politics, the media, the third sector and the communications industry
for their valuable
insights. Our expert interviewees included:
• Dr Keegan McBride, Oxford Internet Institute
• Adeela Warley, CharityComms
• Andrew Bruce Smith, Escherman and the Chartered Institute of
Public Relations
• Dr Mike Katell, The Alan Turing Institute
• Professor Anne Gregory, University of Huddersfield
• Owen Meredith, News Media Association
• Joe Twyman, Deltapoll
• Ed Humpherson, Office for Statistics Regulation
At Demos, we would like to thank our current and former colleagues for
their support.
In particular, thanks to Ellen Judson for her extensive planning and
management of this
project. Thanks also to Lucy Bush, Polly Curtis, Sophia Knight, Billy
Huband-Thompson and
Chloe Burke.
Any errors remain the authors’ responsibility.
Executive Summary
Artificial Intelligence (AI) has been shaping the way we communicate
for many years now: from data-driven microtargeting of campaign messages
to social media algorithms amplifying certain kinds of content. However,
the advent of new forms of generative AI has changed the game with tools
like ChatGPT and DALL-E enabling anyone to create convincing synthetic
content.
AI’s integration in communications offers immense benefits such
as streamlining content creation processes and enhancing engagement.
Yet, these advantages must be tempered by a careful consideration of
the risks. Irresponsible use of AI can propagate false information,
erode trust, and exacerbate societal divides, posing grave implications
for democracy. Fundamentally, AI-generated content can deepen existing
difficulties in distinguishing between what’s real and what’s
not, impacting public trust in the information they consume and even
their engagement with political and democratic processes.
This year is one of the biggest years for democracy in recent decades:
there are elections in India, Mexico, the EU parliament, and presidential
elections in the US, Venezuela, and Taiwan. The UK general election
will come no later than 28th January 2025. This offers political parties
and communication experts new opportunities to create and personalise
content at an unparalleled scale and pace.
Our research reveals a political communications industry poised to adopt
new generative AI technologies at scale in 2024. However, there is a
lack of guidelines and know-how to establish best practice on the use
of AI in communications. The UK has established itself as an international
leader in the field, convening the AI Safety Summit at Bletchley Park
in November 2023 and launching its new AI Safety Institute. But despite
showing this international leadership, there is nothing in place in
the short term to mitigate the risks we face in the coming year regarding
political communications.
In this paper, we explore the current trends in how AI is being used
in communications, how this is changing communications and the benefits
and risks associated with this to communications producers, the public,
the information ecosystem and wider society and democracy. Particularly
we are concerned with communications in politics and government, the
third sector, the media and journalism, and the communications industry.
Based on our extensive research with experts and members of the public,
we make a series of recommendations for politicians, regulators, generative
AI companies and communications professionals to all play their part
in safeguarding trust in our democratic information environments at
this critical juncture in both the technology and our electoral cycles.
A table of these recommendations can be found in Section 3 of this report.
We also set out a framework to guide responsible use of AI in communications,
based on five key principles of AI:
AI
In Banking and Financial Services Article 1 of 4:
Who’s Smarter?
by Rohit Talwar, CEO,
Fast Future
This is the
first in a four part series exploring how Artificial Intelligence (AI)
could impact financial services as its ever smarter capabilities enter
the field over the course of the next few years.
In this first article, I start by examining the current state of play
and consider how AI could outsmart the bankers. Subsequent articles
will go on to look at how AI could change the rules of the game in banking,
how established players can win in this environment, and how new entrants
might fight back and disrupt. The third article will look at the opportunities
that could emerge for incumbents and new entrants, the smart money opportunity,
and the role of AI in enabling enhanced financial inclusion. The fourth
and final article looks at the implications for regulators, central
banks, and managing the ethical and societal challenges.
We are beginning to accept that the rise of AI is inevitable and inescapable.
Everywhere we look, we see stories of how it is – or could soon
be - transforming lives, activities, work, jobs, businesses, sectors,
and nations. The areas receiving the most attention right now are banking
and financial services. This interest is driven by a combination of
the size of the institutions, their wealth, the volume of transactions,
the number of users, and their centrality to life on the planet.
Hence the excitement in the sector over the potential to grow revenues,
cut costs, improve security, enhance customer service, speed up new
developments, innovate around products, develop new pricing models,
tackle fraud, and improve regulatory compliance. Looking a little further
ahead we see even more potential in all of these fields and in the creation
of entirely new asset classes and sectors, in the emergence of smart
savings and intelligent money, and in the potential for radically different
approaches to personalisation, privacy, security, and governance.
In the next few years we can expect AI to evolve rapidly to go beyond
its currently narrow but deep task specific capabilities. Current examples
of such narrow applications include generative applications like ChatGPT
and Bard, a range of robotic process automation (RPA) tools, and highly
specialised solutions in areas such as compliance, fraud, and know your
customer (KYC) verification. These are excellent in their assigned tasks,
but they wouldn’t be very good at each other’s or in other
domains such as drug development, autonomously driving a vehicle, or
making decisions about a bank’s strategy. However, in the coming
years we could see that change with the emergence of artificial general
intelligence (AGI) that is as capable as humans across all our cognitive
capabilities.
The Current State of Play
I n financial services, we are already seeing widespread and accelerating
adoption of AI for consumer facing activities. These include enhancing
service and engagement through personalized banking 24/7 virtual assistants
that provide financial advice and support, help manage accounts, track
spending, and make payments (e.g. Bank of America, HSBC). Other common
applications include streamlining the mortgage lending process (Wells
Fargo).
Others such as Vanguard are using AI wealth management and robo-advisors
to support human financial advisors in giving tailored investment advice
based on individual financial goals and risk tolerance. In investment
banking, the use of AI-enabled big data analytics is being used in algorithmic
trading to make investment decisions and manage risk (BlackRock). Others
are using AI to assess market sentiment through analysis of real-time
news and social media content (Bloomberg).
In order to enhance personalization and engagement, improve retention,
and optimize marketing efforts to maximise customer lifetime value,
machine learning models are frequently used to predict investment behaviour
and product take up (Capital One).
In the ‘back office’, analysis of large historical transactional
datasets is helping to improve efficiency, accuracy, and consistency
in applications such as behaviour prediction and fraud detection and
prevention (Barclays, American Express). Other common uses include interpretation
of loan agreements and contract creation (JP Morgan), risk assessment,
and credit management (Goldman Sachs). For small business loans, some
are going further, by analysing non-traditional data sources to assessing
creditworthiness. The technology is also in common use for regulatory
compliance processes such as anti-money laundering (Standard Chartered),
KYC, and AI-enabled biometric recognition and digital identity verification
(BBVA).
A number of players are also adopting RPA technology to automate repeatable
routine applications such as treasury and cash management (Citibank).
In insurance, AI and chatbots are accelerating and streamlining underwriting
and claims processing (Lemonade).In the crypto assets space, AI is being
used in conjunction with blockchain technology to enhance security and
fraud detection.
Can AI Outsmart the
Bankers?
As confidence with AI increases, growing use can be expected of AI in
areas where bankers simply don’t have the mental bandwidth or resources
available. For example, by combining historic data with complex, non-linear
risk factors, and publicly available market sentiment information such
as social media posts, AI will be able to undertake valuable analytical
and predictive tasks at scale and uncover insights that might not be
readily apparent to humans.
Potential applications include identifying subtle hidden customer behavioral
patterns - such as predicting financial distress or identifying unmet
customer needs. This could lead to better risk management, tightening
of controls, and more accurate pricing and development of micro-personalized
and flexible products and services. Such analysis could also highlight
fraud detection nuances – identifying anomalies and patterns indicative
of complex and sophisticated fraud or money laundering schemes untraceable
by humans.
Internally, AI will help highlight optimization opportunities –
capturing, aggregating, and analysing exponentially increasing volumes
of operational and staff behaviour data across the organisation. This
will help surface inefficiencies and optimization opportunities in processes
like loan approval, customer service, and compliance.
At the strategy level, deep analysis of highly interconnected financial
markets and geopolitical developments could reveal complex correlations
and network effects that could impact systemic risk and financial stability.
Combining such data with insights on technology advances, and on hyperlocal
developments, could help spot emerging risks such as cybersecurity threats.
This analysis could also surface opportunities such as emerging financial
tools and assets before they become mainstream knowledge. Economic and
financial forecasts could become increasingly accurate using ever more
advanced and complex and adaptive predictive AI algorithms. These would
draw on the above insights, coupled with increasingly comprehensive
global economic data to identify leading indicators and patterns that
are not widely recognized.
Rohit Talwar
CEO, Fast Future
Fast
Future is a foresight advisory firm focused on helping clients
harness the trends, shifts, and ideas shaping the future. He has a particular
focus on the implications and applications of rapidly evolving technologies
such as AI.
"Real-Future"
with
Marie-Hélène
Caillol
& Katie Schultz
Marie-Hélène
emphasizes the importance of developing future literacy and integrating
it into the education system. She also discusses the potential of AI
in predicting the future and the need for individuals to understand
their own brains better to improve decision-making. She suggested that
organizations can become more adaptable by embracing collective intelligence.
She is excited to explore the implications of a post-ChatGPT-society
and plans to conduct further research in this area. - AI summary
by Zoom
Moderator
Credits
Marie-Hélène Caillol
Political and future scientist, analyst of the "global systemic
transition", MENA expert, teacher and adviser
Paris, France
President
LEAP - Laboratoire Européen d’Anticipation Politique
Connecting Europe to its citizens, the world and the future www.leap2040.eu
Co-founder and publishing consultant GEAB
The GEAB is your navigation system in a world in transition! Geopolitics,
governance, economy, society, finance, currencies, new technologies,
energy, ... geab.eu/en
Conceptrice et animatrice Pôle Excellence Real-Futur
Comprendre l'avenir en allant le chercher là où il est
présent www.linkedin.com
Moderator
Katie Schultz(Miss Metaverse™)
Futurist and Content Creator
Bangkok, Thailand & Cary, North Carolina, USA missmetaverse.io futuristmm.com
Felix B Bopp
Producer, The Future Now Show Founder & Publisher, Club of Amsterdam clubofamsterdam.com
OpenAI-backed
"AGI ROBOT" shocked the entire Industry
by
Matthew Berman
Humanoid robots making huge advancements was one of my predictions for
2024, and it looks to be coming true. 1x just released new footage of
their NEO robot, powered entirely by a single neural network end-to-end.
Forge Nano to Launch U.S
Battery Manufacturing Business; North Carolina Gigafactory Planned for
2026
Forge Nano, Inc., a leading materials science company that enables peak
performance products through atomic-level surface engineering, today
announced plans to launch a lithium-ion battery manufacturing business
called Forge Battery. The newly formed company intends to produce best-in-class
high-energy and high-power lithium-ion battery cells for defense, aerospace
and specialty electric vehicle markets at a newly constructed Gigafactory
in Raleigh, North Carolina. Forge Battery plans to utilize a U.S-focused
battery material supply chain to further bolster the domestic battery
ecosystem and reduce reliance on foreign suppliers.
The company and outside
investors plan for an initial investment of more than $165M in Morrisville,
North Carolina to build the gigawatt hour-scale battery manufacturing
facility. The Forge Battery facility expects to produce both 21700 and
18650 lithium-ion cells utilizing materials coated with Forge Nano’s
Atomic Armor surface technology.
Forge Nano’s Atomic
Armor will allow Forge Battery to produce first generation high-energy
cells with an expected energy density of 300 Wh/kg with improved safety
and extended lifetime that meet or beat performance of incumbent lithium-ion
technologies. Because Atomic Armor can enhance the performance of existing
and next-generation battery technologies, Forge Battery expects its
cell technology to outperform current and future cell chemistries. Atomic
Armor is also being used to improve production efficiency and drive
down cell costs by removing electrolyte additives and reducing electrolyte
consumption. The facility expects to begin production in 2026.
“Lithium-ion cells
utilizing Forge Nano’s Atomic Armor technology have exceeded our
customer’s expectations, and in turn, increased demand for the
technology that outstrips the current manufacturing capabilities,”
says Paul Lichty, CEO of Forge Nano. “We are extremely excited
to launch Forge Battery in the Battery Belt, where we intend to produce
batteries for the world’s most demanding applications, while showcasing
the power of Forge Nano’s Atomic Armor coating equipment in a large-scale
manufacturing environment.”
Forge Battery’s North
Carolina facility will be facilitated, in part, by a Job Development
Investment Grant (JDIG) approved by the state’s Economic Investment
Committee. The JDIG agreement authorizes the potential reimbursement
of up to $1,525,500, spread over 12 years. Forge Battery also expects
to receive an additional $6,500,000 from the state of North Carolina
based on its current investment projections, which include sales tax
exemptions on planned capital investment.
“North Carolina’s
growing leadership in clean energy can be seen everywhere you look,
and Forge Battery’s decision continues our momentum,” said
North Carolina Governor Roy Cooper. “New jobs, new investment,
and new opportunities for our people are coming fast as we embrace this
vital new sector of the global economy.”
Forge Battery’s facility
is expected to create hundreds of high-paying jobs for Wake County residents.
Although wages will vary depending on the position, the payroll impact
for the project is expected to be more than $16 million each year. Through
the JDIG agreement, new tax revenue generated through Forge Battery’s
operation will go toward helping economically challenged communities
throughout the state.
Accelerating innovative
and beneficiary-centered technological solutions for positive impact
EPFL Tech4Dev aims to implement innovative technological solutions that
can scale and reach a growing number of beneficiaries in the global
South. The initiative is one among a few that explores novel ways for
universities to contribute to the economic, social, and environmental
development in the global South, and advances these current approaches
with its dedicated technological entrepreneurship angle.
Tech4Dev will focus on
providing experience-based education, promoting impactful entrepreneurial
solutions, as well as innovative cross-sector research projects –
all focused on relevant sustainability challenges sourced from Tech4Dev’s
NGO Council.
Futurists
predict a point where humans and machines become one.
But will we see it coming? by John Kendall
Hawkins and Sandy Boucher, University of New England
Most people are familiar with the deluge of artificial
intelligence (AI) apps that seem designed to make us more efficient
and creative. We've got apps that take text prompts and generate art,
and the controversial ChatGPT, which raises serious questions about
originality, misinformation and plagiarism.
Despite these concerns, AI is becoming ever more
pervasive and intrusive. It's the latest technology that will irreversibly
changeour
lives.
The internet and smartphones were other examples.
But unlike those technologies, many philosophers and scientists think
AI could one day reach (or even go beyond) human-style "thinking".
This possibility, coupled with our increasing dependence on AI, is at
the root of a concept in futurism called "technological
singularity".
This term has been around for a while, having been
popularised
by the US science fiction writer Vernor Vinge a few decades ago.
Today, the "singularity" refers to a hypothetical
point in time at which the development of artificial
general intelligence (AGI) - that is, AI with human-level
abilities - becomes so advanced that it will irreversibly change human
civilisation.
It would mark the dawn of our inseparability from
machines. From that moment on, we won't be able to live without them
without ceasing to function as human beings. But if the singularity
comes, will we even notice it?
Brain implants as the first stage
To understand why this isn't the stuff of fairy
tales, we need only look as far as recent developments in brain-computer
interfaces (BCIs). BCIs are a natural beginning to the singularity in
the eyes of many futurists, because they meld mind and machine in a
way no other technology so far can.
Elon Musk's company Neuralink
is seeking
permission from the US Food and Drug Administration to begin
human trials for its BCI technology. This would involve implanting neural
connectors into volunteers' brains so they can communicate instructions
by thinking them.
Neuralink hopes to help paraplegic people walk and
blind people see again. But beyond these goals are other ambitions.
Musk has long
said he believes brain implants will allow telepathic
communication, and lead to the co-evolution of humans and
machines. He argues
that unless we use such technology to augment our intellects, we risk
being wiped out by super-intelligent AI.
Musk is understandably not everyone's go-to for
tech
expertise. But he's not alone in predicting a massive growth
in AI's capabilities. Surveys show AI researchers overwhelmingly
agree AI will achieve human-level "thinking" within
this century. What they don't agree on is whether this implies consciousness
or not, or whether this necessarily means AI will do us harm once it
reaches this level.
Another BCI technology company, Synchron,
has created a minimally invasive implant that allowed a patient with
amyotrophic lateral sclerosis (ALS) to send emails and browse the internet
using his thoughts.
A patient demonstrates the capabilities
of Synchron's interface.
Synchron chief executive Tom Oxley believes brain
implants could ultimately go beyond prosthetic rehabilitation and completely
transform how humans communicate. Speaking to a TED
audience, he said they may one day allow users to "throw"
their emotions so others can feel what they're feeling, and "the
full potential of the brain would then be unlocked".
Early achievements in BCIs could arguably be considered
the first stages of a tumbling towards the postulated singularity, in
which human and machine become one. This need not imply machines will
become "sentient" or control us. But the integration itself,
and our ensuing dependency on it, could change us irrevocably.
It's also worth mentioning that the start-up funding
for Synchron partly
came from DARPA, the research and development arm of the
US Department of Defense that helped gift
the world the internet. It's probably wise to be concerned
about where DARPA places its investment monies.
Would AGI be friend or foe?
According to Ray Kurzweil, a futurist and former
Google innovations engineer, humans with AI-augmented minds could be
thrown onto the autobahn of evolution - hurtling forward without speed
limits.
In his 2012 book How to Create a Mind, Kurzweil
theorises the neocortex
- the part of the brain thought to be responsible for "higher functions"
such as sensory perception, emotion and cognition - is a hierarchical
system of pattern recognisers which, if emulated in a machine, could
lead to artificial super-intelligence.
He predicts the singularity will be with
us by 2045, and thinks it might bring about a world of super-intelligent
humans, perhaps even the Nietzschean "Uebermensch": someone
who surpasses all worldly constraints to realise their full potential.
But not everyone sees AGI as a good thing. The late,
great theoretical physicist Stephen Hawking warned super-intelligent
AI could result
in the apocalypse. In 2014, Hawking told the BBC
the development of full artificial intelligence
could spell the end of the human race. (...) It would take off on
its own and redesign itself at an ever-increasing rate. Humans, who
are limited by slow biological evolution, couldn't compete, and would
be superseded.
Another idea that relates to the singularity is
that of the AI-enabled "hive mind". Merriam-Webster defines
a hive mind as
the collective mental activity expressed in the
complex, coordinated behaviour of a colony of social insects (such
as bees or ants) regarded as comparable to a single mind controlling
the behaviour of an individual organism.
A theory has been developed by neuroscientist Giulio
Tononi around this phenomenon, called Integrated
Information Theory (IIT). It suggests we are all heading
toward a merger of all minds and all data.
Philosopher Philip Goff does a good job of explaining
the implications of Tononi's concept in his book Galileo's
Error:
IIT predicts that if the growth of internet-based
connectivity ever resulted in the amount of integrated information
in society surpassing the amount of integrated information in a human
brain, then not only would society become conscious but human brains
would be 'absorbed' into that higher form of consciousness. Brains
would cease to be conscious in their own right and would instead become
mere cogs in the mega-conscious entity that is the society including
its internet-based connectivity.
It's worth noting there's little evidence such a
thing could ever come to fruition. But the theory raises important ideas
about not only the rapid acceleration of technology (not to mention
how quantum computing might propel this) - but about the nature of consciousness
itself.
Hypothetically, if a hive mind were to emerge, one
could imagine it would mark the end of individuality and the institutions
that rely on it, including democracy.
The final frontier is between our ears
Recently
OpenAI (the company that developed ChatGPT) released a blog post reaffirming
its commitment to achieving AGI. Others will doubtless follow.
Our lives are becoming algorithmically driven in
ways we often can't discern, and therefore can't avoid. Many features
of a technological singularity promise amazing enhancements to our lives,
but it's a worry these AIs are the products of private industry.
They are virtually unregulated, and largely at the
whims of impulsive "technopreneurs" with more
money than most of us combined. Regardless of whether we
consider them crazy, naive, or visionaries, we have a right to know
their plans (and be able to rebut them).
If the past few decades are anything to go by, where
new technologies are concerned, all of us will be affected.
Congressman Ro Khanna offers a revolutionary, “progressive”
(James J. Heckman, Nobel Prize winner and professor of economics at
the University of Chicago) roadmap to facing America’s digital
divide, offering greater economic prosperity to all. In Khanna’s
vision, “just as people can move to technology, technology can
move to people” (from the foreword by Amartya Sen, Nobel Laureate
in Economics) where “Khanna envisions redistributing opportunities
from coastal cities to rural middle-America…An exciting vision,
brilliantly rendered.” (Arlie Russell Hochschild, author of Strangers
in Their Own Land).
Unequal access to technology
and the revenue it creates is one of the most pressing issues in the
United States. An economic gulf exists between those who have struck
gold in the tech industry and those left behind by the digital revolution;
a geographic divide between those in the coastal tech industry and those
in the heartland whose jobs have been automated; and existing inequalities
in the technological access - students without computers, rural workers
with spotty WiFi, and many workers without the luxury to work remotely.
Congressman Ro Khanna’s
Progressive Capitalism tackles these challenges head-on and imagines
how the digital economy can create opportunities for people across the
country without uprooting them. Anchored by an approach Khanna calls
“progressive capitalism,” he shows how democratizing access
to tech can strengthen every sector of economy and culture. By expanding
technological jobs nationwide through public and private partnerships,
we can close the wealth gap in America and begin to repair the fractured,
distrusting relationships that have plagued our country for fall too
long.
Inspired by his own story
born into an immigrant family, Khanna understands how economic opportunity
can change the course of a person’s life. Moving deftly between
storytelling, policy, and some of the country’s greatest thinkers
in political philosophy and economics, Khanna presents a vision we can’t
afford to ignore. Progressive Capitalism is a “practical and aspirational”
(Kimberlé Crenshaw, professor of law at UCLA and Columbia University)
roadmap to how we can seek dignity for every American in an era in which
technology shapes every aspect of our lives.
Ro Khanna
is an American politician and lawyer serving as the U.S. representative
from California's 17th congressional district since 2017. A member of
the Democratic Party, he defeated eight-term incumbent Democratic Representative
Mike Honda in the general election on November 8, 2016, after first
running for the same seat in 2014. Khanna also served as the deputy
assistant secretary in the United States Department of Commerce under
President Barack Obama from August 8, 2009, to August 2011.
Khanna identifies as a progressive capitalist, and has called for a
"new economic patriotism" as a governing philosophy. He states
that he only accepts campaign donations from individuals and is one
of only six members of the U.S. House of Representatives, and ten members
of Congress, who state that they do not take campaign contributions
from political action committees (PACs) or corporations.- Wikipedia
Advanced AI systems are coming under increasing
critcism for how much energy they use. But it's important
to remember that AI could also contribute in various ways to our
response to climate change.
Climate change can be broken down into
several smaller problems that must be addressed as part of an overarching
strategy for adapting to and mitigating it. These include identifying
sources of emissions, enhancing the production and use of renewable
energy and predicting calamities like floods and fires.
My own research looks at how AI can be
harnessed for predicting greenhouse gas emissions from
cities and farms
or to understand changes in vegetation,
biodiversity and terrain
from satellite images.
Here are four different areas where AI
has already managed to master some of the smaller tasks necessary for
a wider confrontation with the climate crisis.
1. Electricity
AI could help reduce energy-related emissions by
more accurately forecasting
energy supply and demand.
AI can learn patterns in how and when people use
energy. It can also accurately forecast how much energy will be generated
from sources like wind and solar depending on the weather and so help
to maximise the use of clean energy.
For example, by estimating the amount of solar power
generated from panels (based on sunlight duration or weather conditions),
AI can help plan the timing of laundry or charging of electric vehicles
to help consumers make
the most of this renewable energy. On a grander scale, it
could help grid operators pre-empt and mitigate gaps in supply.
AI can help make the most of renewable
energy when it is abundant.Kazoka/Shutterstock
Researchers
in Iran used AI to predict the energy consumption of a research
centre by taking account of its occupancy, structure, materials and
local weather conditions. The system also used algorithms to optimise
the building's energy use by proposing appropriate insulation measures
and heating controls and how much lighting and power was necessary based
on the number of people present, ultimately reducing it by 35%.
2. Transport
Transport accounts for roughly one-fifth
of global CO2 emissions. AI models can encourage green travel
options by suggesting the most efficient routes for drivers, with fewer
hills, less traffic and constant speeds, and so minimise emissions.
An AI-based system suggested routes for electric
vehicles in the city of Gothenburg, Sweden. The system used
features like vehicle speed and the location of charging points to find
optimal routes that minimised energy use.
3. Agriculture
Studies
have shown that better farming practices can reduce emissions. AI can
ensure that space and fertilisers (which contribute to climate change)
are used sparingly.
By predicting how much of a crop people will buy
in a particular market, AI can help producers and distributors minimise
waste. A 2017 study conducted
by Stanford University in the US even showed that advanced
AI models can predict county-level soybean yields.
This was possible using images from satellites to
analyse and track the growth of crops. Researchers compared multiple
models to accurately predict crop yields and the best performing one
could predict a crop's yield based on images of growing plants and other
features, including the climate.
Knowing a crop's probable yield weeks in advance
can help governments and agencies plan alternative means of procuring
food in advance of a bad harvest.
Accurate yield predictions can better
prepare countries for food shortages.JBK/Shutterstock
4. Disaster management
The prediction and management of disasters is a
field where AI has made major contributions. AI
models have studied images from drones to predict flood damage
in the Indus basin in Pakistan.
The system is also useful for detecting the onset
of a flood, helping with real-time rescue operation planning. The system
could be used by government authorities to plan prompt relief measures.
These potential uses don't erase the problem of
AI's energy consumption, however, To ensure AI can be a force for good
in the fight against climate change, something will still have to be
done about this.
How is Artificial
Intelligence beneficial for climate change? by
ChatGPT 3.5:
Artificial intelligence
(AI) offers numerous benefits for addressing climate change and facilitating
sustainability efforts:
Energy
Efficiency AI can optimize energy consumption in various sectors such as
transportation, manufacturing, and buildings. Machine learning algorithms
can analyze energy usage patterns, identify inefficiencies, and recommend
strategies for reducing energy consumption, thereby lowering greenhouse
gas emissions.
Renewable
Energy Integration AI enables better integration and management of renewable energy
sources such as solar and wind power. AI algorithms can forecast renewable
energy generation, optimize energy storage systems, and dynamically
adjust energy production and consumption to match supply and demand,
enhancing the reliability and efficiency of renewable energy systems.
Climate
Modeling and Prediction AI techniques,
including machine learning and neural networks, are employed in climate
modeling to improve the accuracy and resolution of climate simulations.
These models help scientists understand complex climate systems, predict
future climate scenarios, and assess the potential impacts of climate
change on various regions and ecosystems.
Natural
Resource Management AI technologies support sustainable natural resource management
by monitoring and analyzing environmental data. For example, AI-powered
remote sensing and satellite imagery can track deforestation, monitor
biodiversity, and assess changes in land use, facilitating conservation
efforts and sustainable land management practices.
Precision
Agriculture AI-driven precision agriculture techniques optimize farming practices
by analyzing data on soil quality, weather conditions, crop health,
and pest infestations. By providing real-time insights and recommendations,
AI helps farmers improve crop yields, reduce resource inputs (such
as water and fertilizers), and minimize environmental impacts.
Climate
Adaptation and Resilience AI can enhance climate resilience by analyzing climate risks and
developing adaptive strategies for vulnerable communities and infrastructure.
AI-powered risk assessment tools can identify areas prone to climate-related
hazards such as floods, storms, and wildfires, enabling proactive
planning and disaster preparedness measures.
Carbon
Capture and Sequestration AI plays a role in advancing carbon capture and sequestration
technologies by optimizing the design and operation of carbon capture
systems, predicting carbon dioxide storage capacity in geological
formations, and developing novel materials for capturing and storing
carbon emissions from industrial processes.
Behavioral
Change and Awareness AI-driven applications and platforms can raise awareness about
climate change, promote sustainable behaviors, and facilitate citizen
engagement in environmental conservation efforts. Personalized recommendations,
interactive tools, and data visualization techniques help individuals
and communities understand their environmental footprint and take
actions to reduce it.
Overall, AI offers innovative solutions and tools for mitigating
climate change, fostering sustainability, and building a more resilient
and environmentally friendly future. By harnessing the power of AI
technologies, we can address the complex challenges posed by climate
change and work towards a more sustainable and equitable world.
Energy
Efficiency
Building Energy Efficiency: AI climate
tech lowering carbon footprints
by BrainBox AI
Imagine a building that's not just responsive
to its internal sensors but also aware of its external environment.
It considers weather conditions, energy sources, costs, and environmental
impact in real-time. How is this possible? Through autonomous optimization
technology that takes HVAC system control to a whole new level!
We dive deep into this cutting-edge solution
created by BrainBox AI, going beyond temperature settings to optimize
individual HVAC components, damper positions, fan speeds, and more.
This granular approach revolutionizes building efficiency, making the
building adapt to its environment minute by minute.
This technology makes hundreds or thousands
of unique optimization decisions daily, tailored to each building's
specific location and external factors. It's like waking up your building
and allowing it to make intelligent decisions. You can now make your
building more efficient and reduce its carbon footprint without the
hassle of installing new sensors or investing in expensive equipment.
Join us on this journey toward a more sustainable
and efficient future! Don't miss out on the building revolution powered
by AI.
Schneider Electric CEO on how AI & other tech can accelerate the
energy transition
by CNBC
Peter Herweck, CEO of Schneider Electric, discusses artificial intelligence,
the energy transition and green hydrogen with CNBC.
ABB and Verdigris Technologies
have developed machine-learning algorithms to predict unplanned peaks
in power consumption – and identify strategies to prevent them
ABB is deploying artificial
intelligence (AI) to help commercial and industrial buildings revolutionize
their energy management and tackle rising electricity peak tariffs.
The company has added two new AI-powered applications to the ABB AbilityTM
Electrical Distribution Control System (EDCS): Energy Forecasting and
Intelligent Alerts.
ABB has developed the AI
functions in partnership with Silicon Valley AI specialist Verdigris
Technologies as part of the company’s Open Innovation program.
The Energy Forecasting app will enable users to reduce their electricity
bills by reducing peak demand charges. The Intelligent Alerts app uses
machine learning algorithms to help customers better manage their assets,
identifying underlying issues before they become problems.
Andrea Temporiti, Digital
Leader for ABB’s Electrification business, said: “Our use
of AI to help customers make better energy management decisions demonstrates
ABB’s commitment to innovation in our products and quality in our
services. With the new Energy Forecasting and Smart Alerts apps, AI
drills down into the facility’s power data to pinpoint actionable
opportunities for productivity improvements and energy cost savings.”
ABB’s Open Innovation
program engages incubators, accelerators, innovation centers and start-ups
in the co-development and design of innovative new digital solutions
and business models. The company is building an ecosystem of innovation
partners to work on digital energy management services for applications
that range from smart buildings to e-mobility. The collaborations help
start-ups to develop services that can be marketed directly to ABB customers
via its Digital Marketplace; the innovation strategy also enables ABB
customers to benefit from cutting-edge digital technologies much sooner.
ABB AbilityTM Energy Forecasting
uses AI to give facility managers accurate power consumption predictions.
Energy Forecasting enables them to take timely action to reduce unplanned
consumption spikes by re-scheduling or switching off non-critical loads
– and taking full advantage of Time of Use (TOU) tariffs.
The Energy Forecasting
AI uses neural network methods to identify and learn patterns in a circuit
or a building’s energy consumption, while also factoring in weather
data. Using weather forecasts and historical data, Energy Forecasting
is then able to predict power consumption (kW) for the next 24 hours,
updating its forecast every 15 minutes with best-in-class accuracy.
“This innovative digital
service makes it easy to take the necessary corrective actions to minimize
any peak demand charges,” said Temporiti. “The precision of
the forecasting reduces hedging positions, narrows variability and produces
meaningful energy cost savings for commercial and industrial buildings.”
ABB AbilityTM Intelligent
Alerts uses machine learning to help customers better manage critical
assets. Intelligent Alerts learns how various factors affect the building
and key assets so that it can minimize the distraction of false alerts
and information overload, allowing facility teams to focus their time
more productively. Intelligent Alerts also identifies the relevant circuits
and makes potential recommendations to ensure any response can be swift
and decisive.
Thomas Chung, Head of Product
Strategy at Verdigris said: “Verdigris AI is 10 times more effective
than traditional energy management methods. Our partnership with ABB
enables our AI capabilities to reach a significantly larger ecosystem
of ABB users. These energy and asset management tools will cut through
the noise to deliver actionable insights, identify real energy savings
and make resource allocation more effective.”
ABB
(ABBN: SIX Swiss Ex) is a technology leader that is driving the digital
transformation of industries. With a history of innovation spanning
more than 130 years, ABB has four customer-??focused, globally leading
businesses: Electrification, Industrial Automation, Motion, and Robotics
& Discrete Automation, supported by the ABB Ability™ digital
platform. ABB’s Power Grids business will be divested to Hitachi
in 2020. ABB operates in more than 100 countries with about 147,000
employees.
Verdigris
Technologies is a leading AI company based in historic NASA
research park in Silicon Valley, California with offices in the US and
Taiwan. They are a privately held company backed by prominent venture
capitalists and have developed several award-winning products for commercial
and industrial energy management.
What is Verdigris?
by Verdigris
ABB Ability™ Efficiency
AI
by ABB Electrification
ABB Ability™ Efficiency AI, powered by BBAI, uses artificial intelligence
(AI) to proactively optimize the HVAC system in buildings in order to
achieve remarkable cost savings, reduce carbon emissions and increase
tenant comfort.
AI-Powered Solution to Optimize Renewable Energy Production byOmdena
Challenge A leading energy
company was facing the challenge of integrating renewable energy sources
into its grid. The company was struggling to predict renewable energy
generation and demand and to develop optimal dispatch plans for its
renewable energy assets. This was leading to energy shortages and blackouts.
Solution The energy company
partnered with Omdena to develop an AI-powered solution to optimize
renewable energy production. Omdena’s team of data scientists and
machine learning engineers developed a variety of machine learning models
to predict renewable energy generation and demand. The machine learning
models were also trained to identify and mitigate potential grid constraints.
Results The AI-powered
solution has been very successful. The solution has helped the energy
company to increase renewable energy generation by 10% and reduce carbon
emissions by 5%. The solution has also improved grid reliability by
reducing the number of energy shortages and blackouts.
Benefits The AI-powered
solution has provided a number of benefits to the energy company, including:
Increased renewable
energy generation
Reduced carbon emissions
Improved grid reliability
Reduced costs
Conclusion The development
and implementation of the AI-powered solution has been a success for
the energy company. The solution has helped the company to achieve its
renewable energy goals and improve its bottom line.
Lessons Learned
There are a few key lessons
that can be learned from this case study:
AI-powered solutions
can be very effective in optimizing renewable energy production.
It is important to collect
and prepare a large and diverse dataset of renewable energy generation
data, weather data, and grid data in order to train accurate and effective
machine learning models.
It is also important
to evaluate the performance of the machine learning models on a held-out
test set before deploying them to production.
By following these steps,
energy companies can successfully develop and implement AI-powered
solutions that will help them to increase renewable energy generation,
reduce carbon emissions, and improve grid reliability.
Omdena’s Role Omdena played
a key role in the development and implementation of the AI-powered solution.
Omdena’s team of data scientists and machine learning engineers
provided the following services:
Data collection and
preparation
Model development and
training
Model evaluation
Model deployment
Omdena also provided the
energy company with access to its AI platform, which made it possible
to develop and deploy the AI-powered solution quickly and efficiently.
Overall, Omdena’s
involvement in the project was essential to its success. Omdena’s
expertise in AI and machine learning, as well as its AI platform, were
critical to the development and implementation of the AI-powered solution.
Traditional power plants provide stable and consistent electricity,
but one of the most troubling downsides is that they contribute substantially
to greenhouse gas emissions. Renewable energy integration has emerged
as a promising solution in the quest for sustainable energy, provided
the sources can integrate into existing grid infrastructure. But what
is renewable energy integration, and how does it work?
This guide closely examines
renewable energy integration, explains how it works, and discusses its
importance.
What is renewable energy integration? Renewable energy
integration is incorporating renewable energy sources into existing
electric power grids. Renewable energy generates power from self-replenishing
sources such as hydropower, geothermal, wind, and solar. These sources
can provide enough energy for a green future if appropriately integrated.
The sources of renewable power differ greatly from fossil fuels. Adding
renewable sources to the existing power grid has many advantages and
obstacles. Unfortunately, it is not as simple as connecting and transmitting
electricity through the infrastructure.
Take wind turbines, for
example. The wind turns propeller-like blades around a rotor. The rotor
spins a generator and creates electricity. The electrical energy passes
through a transformer that converts the voltage so the electricity system
can use it. Wind energy is beneficial because it is a clean, renewable,
and domestic energy source. However, wind power is inconsistent. Although
wind farms are built in the windiest places possible, wind speeds still
vary. As it stands now, wind power is best as a part of a larger renewable
energy strategy.
Geothermal energy is another
form of renewable energy. It uses heat from inside the Earth to warm
buildings and generate electricity. Where geothermal energy is available,
it is a powerful source of energy. However, the most significant disadvantage
is that it is location-specific. Geothermal plants can only really exist
where the energy is accessible. Thus, like wind power, geothermal energy
is best as a part of a larger renewable energy strategy.
How does renewable energy integration work? Let's discuss
how renewable energy integration works to understand the advantages
and challenges further. First of all, the objectives of renewable energy
integration are to diversify the sources of energy generation to reduce
reliance on fossil fuels and increase the power system's resilience.
However, issues arise around consistency and availability. Whether the
energy source is renewable or not, the electricity fed into the grid
must always equal the amount taken out; otherwise, power outages may
occur.
Renewable energy sources
such as water and geothermal can generate energy consistently enough
to meet baseload power needs, but these sources are unavailable or viable
everywhere. Wind and solar are viable options in many places but vary
significantly throughout the day and year.
Now, power grids depend
upon a consistent stream of energy to generate, transmit, and distribute
electricity from power plants to end consumers. On the other hand, renewable
energy sources rely on grid optimization technologies such as advanced
sensors, automation, and real-time data analytics to integrate successfully.
Ultimately, renewable energy integration depends upon intelligent technologies
to optimize energy distribution and elevate grid management efficiency.
Importance of Renewable Energy Integration The International
Energy Agency estimates that renewable sources must generate most global
electricity to reach net-zero emissions by 2050. Renewable energy is
the fastest-growing energy source in the United States, but more progress
must be made to achieve 100% clean electricity by 2035. The strong drive
for renewable energy integration is because:
It helps mitigate climate
change by reducing greenhouse gas emissions and other air pollutants.
It is a self-replenishing
source of energy that does not decrease in availability or require
time to restore.
It supports distributed
energy systems that lessen the power demand on the grid, extending
the infrastructure's lifespan and reducing resource consumption.
It increases energy
security by diversifying and localizing energy sources.
It lowers costs related
to transportation and enhances local economies by increasing the number
of ways communities can contribute energy to the power grid.
Renewable Energy Integration
with Encora By enabling the
integration of cutting-edge technologies such as IoT sensors, machine
learning, and data analytics, we help companies modernize infrastructure,
develop smart grids, and harness the potential of renewables. Our solutions
optimize operations and provide invaluable customer insights through
digital transformation, leading to enhanced sustainability, cost reduction,
and elevated customer experiences. We use intelligent technologies to
optimize energy distribution and elevate grid management efficiency.
Our renewable energy integration solutions seamlessly integrate renewable
energy sources into existing systems, driving sustainable energy adoption.
Fast-growing tech companies
partner with Encora to outsource product development and drive growth.
We are deeply expert in the various disciplines, tools, and technologies
that power the emerging economy, and this is one of the primary reasons
that clients choose Encora over the many strategic alternatives that
they have.
We’ve learned how to predict weather
over the past century by understanding the science that governs Earth’s
atmosphere and harnessing enough computing power to generate global
forecasts. But in just the past three years, AI models from companies
like Google, Huawei, and Nvidia that use historical weather data have
been releasing forecasts rivaling those created through traditional
forecasting methods.
This video explains the promise and challenges
of these new models built on artificial intelligence rather than numerical
forecasting, particularly as it relates to the ability to foresee extreme
weather.
Climate modeling with
AI: Hype or Reality? & Deep learning and the dynamics of physical
processes
AI for Good
Climate modeling with AI:
Hype or Reality?
Climate simulations remain
one of the best tools to understand and predict global and regional
climate change. Uncertainties in climate predictions originate partly
from the poor or lacking representation of processes, such as ocean
turbulence and clouds, that are not resolved in global climate models
but impact the large-scale temperature, rainfall, sea level, etc. The
representation of these unresolved processes has been a bottleneck in
improving climate simulations and projections. The explosion of climate
data and the power of machine learning (ML) algorithms are suddenly
offering new opportunities: can we deepen our understanding of these
unresolved processes and simultaneously improve their representation
in climate models to reduce climate projections uncertainty? This talk
discusses the advantages and challenges of using machine learning for
climate projections. The focus will be on recent work in which we leverage
ML tools to learn representations of unresolved ocean processes –
in particular, learning symbolic expression. Some of this work suggests
that machine learning could open the door to discovering new physics
from data and enhance climate predictions. Yet, many questions remain
unanswered, making the next decade exciting and challenging for ML +
climate modeling for robust and actionable climate projections.
Deep learning and the dynamics
of physical processes
Deep learning has been
studied for a few years for the modeling of complex physical processes
in industrial fields such as aeronautics or energy production and in
scientific fields such as environment or health. This area of
research, although still emerging, is rapidly gaining momentum and developing
as an interdisciplinary field. It raises new challenges for the interaction
between machine learning and physics. This talk will focus on deep learning
approaches for modeling dynamic physical systems and illustrate three
main challenges: incorporating prior physical knowledge into learning
models, generalizing learning models to multiple environments, and learning
models operating continuously in space and time thus allowing flexible
extrapolation at arbitrary spatiotemporal locations. This presentation
will be illustrated by applications in different domains.
Speakers:
Laure Zanna, Courant Institute,
New York University
Patrick Gallinari, Sorbonne
University
Moderator:
Markus
Reichstein, Max Planck Institute for Biogeochemistry
Join
the Neural Network!
The AI for Good networking
community platform powered by AI.
Designed to help
users build connections with innovators and experts, link innovative
ideas with social impact opportunities, and bring the community together
to advance the SDGs using AI.
FS Talks! The Future
of AI in Natural Resource Management by Purdue FNR
Dr. Jingjing Liang shares
his research using artificial intelligence in forestry in this edition
of the FS Talks Eastern Region seminar series for the U.S. Forest Service.
The talk "The Future of AI in Natural Resource Management –
the Self Learning Forest Growth Model” was given on April 4, 2023.
As a certified forester
and seasoned quantitative ecologist, Liang has been working on the interface
between artificial intelligence and its applications in forest sciences.
His research connects machine learning and big data in the study of
fundamental questions of biodiversity and ecosystem processes, as well
as ecological and socioeconomic impacts of biological conservation.
How AI is Transforming
Remote Sensing Applications
by Geospatial World
Artificial Intelligence today is transforming remote sensing by improving
data processing accuracy and speed. Let’s look at how AI is used
in different remote sensing applications.
Applications of Remote Sensing in Precision
Farming
by Geospatial World
What Is Precision Agriculture?
by PostHarvest Technologies
Precision Agriculture means using exact science & methods to produce
more for less. It involves observing multiple changes in the environment
and its effects on all the growing stages of produce over past experiments.
Optimising the flow of information about
agriculture and rural policy within the EU is what the EU’s Common
Agricultural Policy (CAP) Network is all about.
The Network is a forum
through which National CAP Networks, organisations, administrations,
researchers, entrepreneurs and practitioners can share knowledge and
information (e.g. via peer-to-peer learning and good practices) about
agriculture and rural policy.
The European Commission
set up the EU CAP Network in line with the Regulation of the European
Parliament and of the Council to support CAP
strategic plans (CSPs).
Artificial intelligence
(AI) can make substantial contributions to climate-resilient and low-emissions
development. UN Climate Change’s Initiative on Artificial Intelligence
for Climate Action (#AI4ClimateAction)
explores the role of AI as a powerful tool for advancing and scaling
up transformative climate action in developing countries.
Artificial intelligence
(AI) is already making inroads worldwide in health, education and industry,
but how can this cutting-edge technology help the world combat and mitigate
the effects of climate change?
The recent launch of the UN-led AI
Advisory Body advanced a growing global trend to harness
machine learning to find solutions to common challenges. AI is upping
the data crunching game and a growing number of governments, businesses
and civil society partners are working together to reap its many benefits.
That includes speeding
up and scaling efforts to realize such global ambitions as the 2030
Agenda and its 17 Sustainable
Development Goals (SDGs), which serve as the world’s
blueprint to make the planet greener, cleaner and fairer.
Using AI to Build Climate Resilience Where It's Needed Most
by Boston Consulting Group
87% of public and private sector leaders who oversee climate and #AI
topics believe that AI is an asset in the fight against #ClimateChange.
But there are challenges
to implementation at scale, like lack of funding, training, and access
to decision-makers.
Learn
more about
how we can overcome these challenges and use AI to build climate resilience
in the areas that need it most.
Project Greensand | Carbon
Capture and Storage
by INEOS
A world first, Project Greensand, shows
that Carbon Capture and Storage can work after CO2 from Belgium is successfully
captured, transported and stored under the Danish North Sea.
By 2030, Project Greensand could safely
store up to 8 million tonnes of CO2 a year, which is the equivalent
of 40% of Denmark’s emission reduction target.
Project
Greensand
is working towards enabling Denmark to use CO2 storage as part of the
solution to the climate challenges.
The consortium consists
of 23 Danish and international partners who contribute with expertise
from transportation, storage and monitoring of CO2 in the subsoil. The
consortium's members include everything from Danish and international
companies, research institutes, universities, and start-ups.
We are already underway with a green transformation of Denmark. Solar
cell panels are being installed, offshore wind farms are being built,
and we are replacing petrol and diesel cars with electric cars. These
are all steps in the right direction - but it is not enough. If we are
to solve the climate challenges and achieve the objectives of the Paris
Agreement, we must also capture CO2 from emissions and store it.
The United Nation’s Intergovernmental
Panel on Climate Change (IPCC) points to CO2 capture and storage as
an effective tool for rapidly reducing our CO2 emissions, and in Denmark
we have decided that CO2 storage must be a key part in achieving our
climate goals. Project Greensand is the most mature project for storage
of CO2 in Denmark with the possibility of storing up to 1,5 million
tonnes of CO2 per year in 2025/2026 and up to 8 million tonnes of CO2
per year in 2030. This corresponds to emissions from approximately 725.000
Danes a year - or more than 13% of Denmark's annual CO2 emissions.
Project Greensand is supported by the Danish
state through the Energy Technology Development and Demonstration Program
- EUDP.
The goal for Project Greensand is to help establish
a value chain for transportation and geological CO2 storage offshore
in Denmark in 2025/2026. The project is currently in the pilot phase,
which is called phase 2. Here the project is developed and demonstrated.
The first CO2 to be stored in the North Sea will be sailed from Antwerp
in Belgium to the Nini platform in the North Sea. Here it is sent underground
via the existing offshore platform and a dedicated well for the purpose.
The final stop for the CO2 is in a sandstone reservoir 1800 meters below
the seabed, where it is permanently stored.
Geological and production
data on the Nini field have been collected for more than 20 years. This
means that the consortium's leading partners INEOS and Wintershall Dea
know the structures of the underground extremely well. All this data
is important knowledge when the CO2 is to be sent underground - and
subsequently monitored carefully to ensure efficient and secure storage.
Using AI to listen to
all of Earth's Species
by Earth Species Project (ESP)
A presentation on decoding animal language by Aza Raskin, followed by
a panel discussion hosted at the World Economic Forum in San Francisco
on October 25, 2022 exploring how AI is expanding our ability to understand
other species and the benefits and potential challenges that this new
understanding will bring.
Featuring Kay Firth-Butterfield,
Head of AI at the WEF; Aza Raskin, President and Cofounder, Earth Species
Project; Dr. Karen Bakker, Professor at University of British Columbia
and Fellow at Harvard Ratcliffe Institute; Dr. Ari Friedlaender, Professor
at University of California, Santa Cruz
We are thinkers, makers,
AI research scientists, conservation technologists, learners, entrepreneurs
and artists who care deeply about the interdependent future of our planet
and its species.
Our work to decode communication of other species builds on the extraordinary
advances we are seeing in AI being applied to human language,
which we are extending to the non-human world. It also builds off many
decades of bioacoustics and behavioral ecology research which has already
uncovered complex communication systems in other species.
The machine learning models
we are building are also supporting and deepening ongoing research into
the behavior of other species and advancing conservation efforts on
the ground today.
All our work is inspired
by current challenges being faced in conservation biology. It is also
all publicly accessible, helping to ensure researchers no longer need
to reinvent the wheel for each project, and can focus precious resources
on ensuring we have forward momentum as we seek to listen, to understand
and, ultimately, to protect.
The Technology The field of
AI and natural language processing has seen exponential growth in the
last 10 to 15 years.
In 2013 there was a significant
breakthrough with a new machine learning technique that could learn
a geometric representation of an entire language, like a galaxy where
each star is a word and the distance and direction between stars encodes
relational meaning.
Using Artificial Intelligence
to Visualize the Impacts of Climate Change
January 2021, IEEE Computer Graphics and Applications
Authors
Alexandra Luccioni, Victor Schmidt, Vahe Vardanyan, Yoshua Bengio, Université
de Montréal, Mila, Ubisoft, Montréal , Canada
Abstract
Public awareness and concern about climate change often do not match
the magnitude of its threat to humans and our environment. One reason
for this disagreement is that it is difficult to mentally simulate the
effects of a process as complex as climate change and to have a concrete
representation of the impact that our individual actions will have on
our own future, especially if the consequences are long term and abstract.
To overcome these challenges, we propose to use cutting-edge artificial
intelligence (AI) approaches to develop an interactive personalized
visualization tool, the AI climate impact visualizer. It will allow
a user to enter an address - be it their house, their school, or their
workplace - and it will provide them with an AI-imagined possible visualization
of the future of this location in 2050 following the detrimental effects
of climate change such as floods, storms, and wildfires. This image
will be accompanied by accessible information regarding the science
behind climate change, i.e., why extreme weather events are becoming
more frequent and what kinds of changes are happening on a local and
global scale.
Behaviour Change - Behaviour
Change and Public Engagement | Environment 101
by Civil Service Climate + Environment Network
How are people's behaviours contributing to environmental change? How
can we make positive changes to societal habits? What effect can an
individual even make to our climate's future?
CSEN Environment 101 explains
and explores the biggest issues in environmental policy in short and
accessible videos. This video details the types of damaging behaviour
people engage in, the effect this has on the environment, and what behavioural
changes will be needed to reach net zero.
David
Shrier is a futurist and
innovation catalyst with expertise in artificial intelligence, data/analytics,
fintech, digital identity, cybersecurity, and collaborative innovation.
He leads venture studio Visionary Future LLC, helping Global 2000 companies
create breakout growth as well as incubating new technology-enabled
businesses.
David led a SPAC that trades on the NYSE under the ticker ADEX into
its announced merger with Griid Infrastructure*. David is a Senior Advisor
to Dandelion Science, an AI healthtech company that treats the brain
as a data processing engine to address mental health issues; Kaleidoco,
a revolutionary artificially intelligent Augmented Reality (AI/AR) company;
and The Emissions Capture Company (ECCO), a machine learning-driven
carbon utilization provider.
David holds an appointment as Professor of Practice, Imperial College
Business School, Imperial College London, and is a Visiting Scholar
with the GEAR Lab at the School of Engineering, Massachusetts Institute
of Technology. He previously was an Associate Fellow with Saïd
Business School, University of Oxford, and a Lecturer & Futurist
at the Massachusetts Institute of Technology, where he also held positions
as Managing Director, Connection Science and New Ventures Officer, MIT
Sloan. David specializes in helping established organizations to build
new revenue and new markets. Notable engagements include Kyriba, Dun
& Bradstreet, Wolters Kluwer, Ernst & Young, The Massachusetts
Institute of Technology, GE/NBC, The Walt Disney Company, AOL Verizon,
and Starwood, as well as private equity funds. He has led a number of
private equity and venture capital-backed companies as CEO, CFO or COO,
in either interim or full-time capacities, and has cofounded four AI-enabled
MIT spinout enterprises.
David sits on the European Parliament's advisory committee for artificial
intelligence, having provided input into the EU AI Act. He served as
a founding member of the FinTech Industry Committee for FINRA, the U.S.
securities industry's self-regulatory body; advisor to the European
Parliament on distributed ledger applications and policy; a senior advisor
to Revenue & Customs (UK HMRC), and on the Fintech Trade & Investment
Steering Board for the Department of International Trade. He also informally
engages with OECD, Bank of England, FCA, SEC, US Treasury, and FDIC
on innovation, cybersecurity, digital identity, blockchain, and AI.
David is on the advisory board of WorldQuant University, a program offering
a totally-free, accredited, online Master’s degree in financial
engineering.
David has previously counseled the Commonwealth of Nations (the 53 countries
formerly comprising the British Empire) on fintech and innovation policy,
having served as principal author on their 2020 Commonwealth Fintech
Toolkit; was a member of the European Commission-affiliated High Level
Group on citizenship innovation; Copper Technologies (UK) Ltd, a markets
infrastructure provider for institutional crypto trading, during its
growth from Series A to Series C unicorn valuation; Millennium Advisors,
a middle market credit liquidity provider, on founder succession, growth
strategy, corporate culture, and technology trends; helped realign strategy
for Mode Global Holdings plc (LSE: MODE), one of only three FCA-licensed
crypto wallet providers, as a non-executive director; the Government
of Dubai on blockchain and digital identity; the OECD, on blockchain
policy and standards; and the European Commission on commercializing
innovation across Europe.
David and MIT Professor Alex “Sandy” Pentland have published
books including Global Fintech (MIT Press; winner of Outstanding Academic
Title from the American Library Association's Choice Awards), as well
as Trusted Data (MIT Press), New Solutions for Cybersecurity (MIT Press),
Frontiers of Financial Technology, and Trust::Data. David’s monographs
include Basic Blockchain (2020) and Augmenting Your Career: How to Win
at Work in the Age of AI (2021), both from Little Brown.
David’s writing has appeared in CNBC.com, Newsweek.com, Forbes.com
and Coindesk.com, and he also co-edits, together with Professor Pentland,
the Connection Science imprint of MIT Press. He was named 2018 Global
Fintech Person of the Year by Fintech Galaxy, a Refinitiv Top 100 Social
Influencer in Financial Technology, and listed on One World Identity’s
Top 100 People for both 2017 and 2019.
David Shrier was granted an Sc.B. from Brown University in Biology and
Theatre, and worked professionally in theatre after college.
David L. Shrier - Visionary
Future
by Hub Culture Studio Davos 2023
Hub Culture Executive Editor Edie Lush interviews David L. Shrier -
Managing Director at Visionary Future about the Metaverse, and how it
fits into the idea of collaboration in a fragmented world. He talks
about the effects of isolation, particularly during the pandemic, and
how the Metaverse helped people to escape, and what exactly the Metaverse
means, and what its opportunities are.
Alice Dawson
James Ball
John Kendall Hawkins
Sandy Boucher
Lakshmi Babu Saheer
