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La veille de la cybersécurité

#artificialintelligence

I's strength lies in its predictive prowess. Fed enough data, the conventional thinking goes, a machine learning algorithm can predict just about anything -- for example, which word will appear next in a sentence. Given that potential, it's not surprising that enterprising investment firms have looked to leverage AI to inform their decision-making. There's certainly plenty of data that one might use to train an AI-powered due diligence or investment recommendation tool, including sources like LinkedIn, PitchBook, Crunchbase, Owler and other third-party data marketplaces. With it, AI-driven financial research platforms claim to be able to predict the ability of a startup to attract investments, and there might be some truth to this.


The Future of Artificial Intelligence

#artificialintelligence

"In general it is very difficult to build AI that works well for the kinds or kind groups we want it," Ries says in an interview with Fast Company. "There are many cases where you need really specific decisions made about how machine learning should operate." This might be true if there's just one particular group getting trained; but given enough time period machines can figure out what sort people like more, humans will eventually adapt better than any social agents could ever create (not to mention predict when they'll find something useful). Artificial intelligence (AI) is defined as machine learning. Machine Learning is a field in artificial intelligence that involves using computer programs to teach computers how to learn without being explicitly programmed.


SoftBank Reports Record $23 Billion Quarterly Loss as Tech Downturn Hits

WSJ.com: WSJD - Technology

TOKYO--Japanese technology investor SoftBank Group Corp. on Monday reported a record quarterly loss of more than $23 billion after its Vision Fund investments suffered from the global selloff in technology shares. The April-June loss was about 1½ times the previous record set just three months earlier in the January-March quarter.


Growing demand for data science and its mulitple applications

#artificialintelligence

Data science is a branch of information technology that deals with the analysis and processing of large volumes of data, which may be structured or unstructured or a mix of both, in order to find unseen patterns and derive meaningful information. Data science is useful to identify market opportunities, for process optimisation and cost reduction, and to identify abnormal financial transactions, among others. A typical project involves components that require expertise from several of these areas in combinations of varying proportions from one project, to another. As technology finds its way into all our daily activities, so do the digital data trails we leave behind, be it at retail outlets, banks and many other places. Many organisations have realised they are sitting on a veritable gold mine of information that they can capitalise on and put to good use.


How Machine Learning Can Be Used For Cryptocurrency Trading

#artificialintelligence

Many predict a great future for machine learning and artificial intelligence. However, the best developments in this direction belong either to the academic community or too big business, mainly in the field of advertising. Therefore, there are not many working projects that would allow cryptocurrency traders to use artificial intelligence in their service. Let's figure out how the principle of machine learning works in cryptocurrency trading, and also consider one of the options for automatic trading. And in the next article, we will create and train our own bot, which in theory is able to show a positive result, however, its use is highly discouraged.


Why not all VCs are ready to embrace AI-powered investment tools – TechCrunch

#artificialintelligence

AI's strength lies in its predictive prowess. Fed enough data, the conventional thinking goes, a machine learning algorithm can predict just about anything -- for example, which word will appear next in a sentence. Given that potential, it's not surprising that enterprising investment firms have looked to leverage AI to inform their decision-making. There's certainly plenty of data that one might use to train an AI-powered due diligence or investment recommendation tool, including sources like LinkedIn, PitchBook, Crunchbase, Owler and other third-party data marketplaces. With it, AI-driven financial research platforms claim to be able to predict the ability of a startup to attract investments, and there might be some truth to this.


Hitting the Books: How much that insurance monitoring discount might really be costing you

Engadget

Machine learning systems have for years now been besting their human counterparts at everything from Go and Jeopardy! to drug discovery and cancer detection. With all the advances that the field has made, it's not unheard of for people to be wary of robots replacing them in tomorrow's workforce. These concerns are misplaced, argues Gerd Gigerenzer argues in his new book How to Stay Smart in a Smart World, if for no other reason than uncertainty itself. AIs are phenomenally capable machines, but only if given sufficient data to act on. Introduce the acutely fickle precariousness of human nature into their algorithms and watch their predictive accuracy plummet -- otherwise, we'd never have need to swipe left. In the excerpt below, Gigerenzer discusses the hidden privacy costs of sharing your vehicle's telematics with the insurance company.


This Is How I Used Artificial Intelligence in My Life During the Last 24 Hours

#artificialintelligence

What can we do in 24 hours? What happens in our lives between sunrise and sunset? What happens in 24 hours around the world? On average, in 24 hours, I will experience 104,000 heartbeats, I'll take a breath about 23,000 times, I'll walk about 8,000 steps on average, and in the shower, I'll spend about 12 minutes. My body will shed and create up to 50 trillion new cells, and I usually spend 20 minutes in the bathroom. There will be a 0.35 mm growth in my hair, and I will also lose somewhere between 40 and 100 hairs at the same time, and on average, I'll speak for roughly 48,000 words.


Link Machine Learning (LML): How Risky is It Saturday?

#artificialintelligence

Link Machine Learning achieves a high risk analysis based on InvestorsObserver research. The proprietary system gauges how much a token can be manipulated by analyzing much money it took to shift its price over the last 24 hour period along with analysis of recent changes in volume and market cap. The gauge is between 0 and 100 with lower scores equating to higher risk while higher values represent lower risk.


Remote C++ Developer openings in Boston on August 06, 2022

#artificialintelligence

Role requiring'No experience data provided' months of experience in None Piper Companies is seeking a C Developer for a full-time, fully remote opportunity for a cutting-edge tech research foundation based out of Chapel Hill, NC. The organization has remained a leading data science and devops research partner since its founding in 2004. The C Developer will join a team dedicated to supporting an open-source data management software utilized globally by research, commercial, and government clients. Responsibilities of the C Developer: • Collaborate with cross-functional teams in order to assist with new features, issues, and customer support for the open-source server • Participate in the design, implementation, support, documentation, and testing of the clients and the server • Manage the strategic development of new and existing plugins • Comfortable working with infrastructure and containers. Qualifications of the C Developer: • 1 – 4 years of software development experience • Strong development experience using C • Strong familiarity with Linux and the command line • Bachelor's Degree is required • Applicants must be a resident of North Carolina in order to be considered • Must hold U.S. citizenship or green card visa.