This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
AI and machinelearning are poised to drive innovation across multiple sectors, particularly government, healthcare, and finance. AI and machinelearning evolution Lalchandani anticipates a significant evolution in AI and machinelearning by 2025, with these technologies becoming increasingly embedded across various sectors.
AI and MachineLearning will drive innovation across the government, healthcare, and banking/financial services sectors, strongly focusing on generative AI and ethical regulation. How do you foresee artificial intelligence and machinelearning evolving in the region in 2025?
According to Jyoti, AI and machinelearning are leading the way in sectors such as government, healthcare, and financial services. Jyoti Lalchandani, Regional Managing Director, META, Central Asia & India, IDC shared her perspective on the technology trends set to define the Middle Easts digital transformation.
based company, which claims to be the top-ranked supplier of renewable energy sales to corporations, turned to machinelearning to help forecast renewable asset output, while establishing an automation framework for streamlining the company’s operations in servicing the renewable energy market. To achieve that, the Arlington, Va.-based
Energy Information Administration forecasts 47% higher global energy demand by 2050. [1] 2] But by 2050, as we collectively seek to meet net-zero targets, 90% of the world’s electricity is predicted to come from renewable sources. [3] 3] (Download our infographic to learn more about recent trends.)
As part of this transition, the company is aiming for a net-zero carbon footprint by 2050. Historically, AI use has been focused on machinelearning in operations such as exploration and drilling in the initial phases of energy production.
million sq km over six countries and is the world’s largest tropical carbon sink — by applying machinelearning to parse satellite imagery in order to be able identify illegal logging activity in real time. so they’re armed with actionable intelligence to combat deforestation and biodiversity loss.
In fact, more than 3,200 companies have set science-based carbon targets , and thousands of companies from around the world are pledging to reach net-zero emissions by either 2040 or 2050. Natural resources: In addition to reducing their carbon footprint, companies need to address water usage and improve waste management practices.
“The beauty of [our approach] is if you scale it up across the tonnage that’s been processed in the world today it’s a very scalable business model — if we were to just focus on this data-as-a-service business but our ambitions don’t stop there,” says Stocker.
This number is concerning given emerging digital technologies such as blockchain, IoT, artificial intelligence, and machinelearning are increasing demand for data centre services further, as workloads are no longer confined to the core data centre and can run anywhere, including the edge.
Together these measures, all enabled by smarter digital tools, can have a tangible impact on closing the net-zero gap by 2050. Its the aggregation, collation, and interpretation of data that is key. With the Energy Command Center, this data supports decision-making for resource optimization and decarbonization.
trillion by 2050. We aggregate and harmonize data from multiple sources, applying climate data science and machinelearning on Google Cloud to deliver insights in Google Looker. He works closely with global executives to accelerate their net-zero transition through enhanced climate risk modeling. trillion and $3.1
This allows for an omni-channel view of the customer and enables real-time data streaming and a safe zone to test machinelearning models using Cloudera Data Science Workbench (CDSW). Failure to address this meant major implications for the IRS and the taxpayer. People First . Data for Good.
At the same time, the Net-Zero Banking Alliance was also launched. The industry-led alliance brings together 45 banks from 24 countries, which are “committed to aligning their lending and investment portfolios with net-zero emissions by 2050.” President Biden announced at the climate summit that the U.S.
We organize all of the trending information in your field so you don't have to. Join 49,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content