Goto

Collaborating Authors

Results


Three opportunities of Digital Transformation: AI, IoT and Blockchain

#artificialintelligence

Koomey's law This law posits that the energy efficiency of computation doubles roughly every one-and-a-half years (see Figure 1–7). In other words, the energy necessary for the same amount of computation halves in that time span. To visualize the exponential impact this has, consider the face that a fully charged MacBook Air, when applying the energy efficiency of computation of 1992, would completely drain its battery in a mere 1.5 seconds. According to Koomey's law, the energy requirements for computation in embedded devices is shrinking to the point that harvesting the required energy from ambient sources like solar power and thermal energy should suffice to power the computation necessary in many applications. Metcalfe's law This law has nothing to do with chips, but all to do with connectivity. Formulated by Robert Metcalfe as he invented Ethernet, the law essentially states that the value of a network increases exponentially with regard to the number of its nodes (see Figure 1–8).


Stock Forecast Based On a Predictive Algorithm

#artificialintelligence

This Chemicals Stocks forecast is designed for investors and analysts who need predictions of the best chemical stocks to buy for the whole Chemistry Industry (see Chemicals Stocks Package). Package Name: Chemicals Stocks Recommended Positions: Long Forecast Length: 1 Year (6/4/21 – 6/5/22) I Know First Average: 43.01% OXY was our the best stock pick with a return of 140.06%. Other notable stocks were VHI and MOS with a return of 77.09% and 59.64%. The package had an overall average return of 43.01%, providing investors with a premium of 45.02% over the S&P 500's return of -2.01%


Stock Forecast Based On a Predictive Algorithm

#artificialintelligence

This Automotive Stocks forecast is designed for investors and analysts who need predictions of the best-performing stocks in the automotive industry (see Automotive Stocks Package). Package Name: Automotive Stock Forecast Recommended Positions: Long Forecast Length: 7 Days (5/26/22 – 6/2/22) I Know First Average: 10.83% Several predictions in this 7 Days forecast saw significant returns. The algorithm had correctly predicted 10 out of 10 stock movements. The greatest return came from MOD at 37.7%. AXL, and KWR had notable returns of 25.18% and 15.78%.


Stock Forecast Based On a Predictive Algorithm

#artificialintelligence

This Stock Pickers forecast is designed for investors and analysts who need predictions of the best utilities stocks to buy for the whole Industry. Package Name: Utilities Stocks Recommended Positions: Long Forecast Length: 7 Days (5/18/22 – 5/25/22) I Know First Average: 3.35% For this 7 Days forecast the algorithm had successfully predicted 9 out of 10 movements. The highest trade return came from CDZI, at 15.43%. Further notable returns came from PNW and NRG at 4.36% and 3.65%, respectively. The package had an overall average return of 3.35%, providing investors with a premium of 6.04% over the S&P 500's return of -2.69% during the same period.


The case for placing AI at the heart of digitally robust financial regulation

#artificialintelligence

"Data is the new oil." Originally coined in 2006 by the British mathematician Clive Humby, this phrase is arguably more apt today than it was then, as smartphones rival automobiles for relevance and the technology giants know more about us than we would like to admit. Just as it does for the financial services industry, the hyper-digitization of the economy presents both opportunity and potential peril for financial regulators. On the upside, reams of information are newly within their reach, filled with signals about financial system risks that regulators spend their days trying to understand. The explosion of data sheds light on global money movement, economic trends, customer onboarding decisions, quality of loan underwriting, noncompliance with regulations, financial institutions' efforts to reach the underserved, and much more. Importantly, it also contains the answers to regulators' questions about the risks of new technology itself. Digitization of finance generates novel kinds of hazards and accelerates their development. Problems can flare up between scheduled regulatory examinations and can accumulate imperceptibly beneath the surface of information reflected in traditional reports. Thanks to digitization, regulators today have a chance to gather and analyze much more data and to see much of it in something close to real time. The potential for peril arises from the concern that the regulators' current technology framework lacks the capacity to synthesize the data. The irony is that this flood of information is too much for them to handle.


Stock Forecast Based On a Predictive Algorithm

#artificialintelligence

This Chemicals Stocks forecast is designed for investors and analysts who need predictions of the best chemical stocks to buy for the whole Chemistry Industry (see Chemicals Stocks Package). Package Name: Chemicals Stocks Recommended Positions: Long Forecast Length: 1 Year (5/23/21 – 5/23/22) I Know First Average: 41.59% For this 1 Year forecast, the algorithm had successfully predicted 9 out of 10 movements. OXY was the top-performing prediction with a return of 160.43%. Additional high returns came from VHI and MOS, at 79.37% and 72.02% respectively.


ML for Algorithmic Trading, with Stefan Jansen

#artificialintelligence

Listen to this episode on Anchor FM. Stefan has been a partner in an investment firm where he assisted in building data infrastructure and predictive analytics practice. He accomplished this when data science was only beginning to be taken seriously in the investment industry. You won't want to miss this opportunity to learn from Stefan's experiences. Machines learning from data will continually improve in achieving performance measures.


Top 10 Python Code Generators that Data Scientists Should Know

#artificialintelligence

Python code generators are in high demand in the data science world for completing multiple data science projects. Code generation tools help with productivity, simplification, consistency, and portability in data science projects. Data scientists are leveraging Python code generators including two issues such as maintenance and complexity. Let's explore some of the top Python code generators for data science projects to be used by data scientists efficiently in 2022. PyTorch is one of the top Python code generators for data scientists as an open-source machine learning framework to help in research prototyping as well as a production deployment.


Government Deep Tech 2022 Top Funding Focus Explainable AI, Photonics, Quantum

#artificialintelligence

DARPA, In-Q-Tel, US National Laboratories (examples: Argonne, Oak Ridge) are famous government funding agencies for deep tech on the forward boundaries, the near impossible, that have globally transformative solutions. The Internet is a prime example where more than 70% of the 7.8 billion population are online in 2022, closing in on 7 hours daily mobile usage, and global wealth of $500 Trillion is powered by the Internet. There is convergence between the early bets led by government funding agencies and the largest corporations and their investments. An example is from 2015, where I was invited to help the top 100 CEOs, representing nearly $100 Trillion in assets under management, to look ten years into the future for their investments. The resulting working groups, and private summits resulted in the member companies investing in all the areas identified: quantum computing, block chain, cybersecurity, big data, privacy and data, AI/ML, future in fintech, financial inclusion, ...


AI/ML, Data Science Jobs #hiring

#artificialintelligence

StormGain is a crypto trading platform for everyone. It's a convenient solution for those who want to profit from either the growth or decline of the cryptocurrency market and from long-term investments in crypto assets.