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Category: robotics/AI – Page 1,959

Why AI is the future of fraud detection

The accelerated growth in ecommerce and online marketplaces has led to a surge in fraudulent behavior online perpetrated by bots and bad actors alike. A strategic and effective approach to online fraud detection will be needed in order to tackle increasingly sophisticated threats to online retailers.

These market shifts come at a time of significant regulatory change. Across the globe, new legislation is coming into force that alters the balance of responsibility in fraud prevention between users, brands, and the platforms that promote them digitally. For example, the EU Digital Services Act and US Shop Safe Act will require online platforms to take greater responsibility for the content on their websites, a responsibility that was traditionally the domain of brands and users to monitor and report.

Can AI find what’s hiding in your data? In the search for security vulnerabilities, behavioral analytics software provider Pasabi has seen a sharp rise in interest in its AI analytics platform for online fraud detection, with a number of key wins including the online reviews platform, Trustpilot. Pasabi maintains its AI models based on anonymised sets of data collected from multiple sources.

Using bespoke models and algorithms, as well as some open source and commercial technology such as TensorFlow and Neo4j, Pasabi’s platform is proving itself to be advantageous in the detection of patterns in both text and visual data. Customer data is provided to Pasabi by its customers for the purposes of analysis to identify a range of illegal activities — - illegal content, scams, and counterfeits, for example — - upon which the customer can then act.

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Machine-learning system flags remedies that might do more harm than good

The system could help physicians select the least risky treatments in urgent situations, such as treating sepsis.

Sepsis claims the lives of nearly 270,000 people in the U.S. each year. The unpredictable medical condition can progress rapidly, leading to a swift drop in blood pressure, tissue damage, multiple organ failure, and death.

Prompt interventions by medical professionals save lives, but some sepsis treatments can also contribute to a patient’s deterioration, so choosing the optimal therapy can be a difficult task. For instance, in the early hours of severe sepsis, administering too much fluid intravenously can increase a patient’s risk of death.

To help clinicians avoid remedies that may potentially contribute to a patient’s death, researchers at MIT and elsewhere have developed a machine-learning model that could be used to identify treatments that pose a higher risk than other options. Their model can also warn doctors when a septic patient is approaching a medical dead end — the point when the patient will most likely die no matter what treatment is used — so that they can intervene before it is too late.

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Machine learning speeds up vehicle routing

Strategy accelerates the best algorithmic solvers for large sets of cities.

Waiting for a holiday package to be delivered? There’s a tricky math problem that needs to be solved before the delivery truck pulls up to your door, and MIT researchers have a strategy that could speed up the solution.

The approach applies to vehicle routing problems such as last-mile delivery, where the goal is to deliver goods from a central depot to multiple cities while keeping travel costs down. While there are algorithms designed to solve this problem for a few hundred cities, these solutions become too slow when applied to a larger set of cities.

The solver algorithms work by breaking up the problem of delivery into smaller subproblems to solve — say, 200 subproblems for routing vehicles between 2,000 cities. Wu and her colleagues augment this process with a new machine-learning algorithm that identifies the most useful subproblems to solve, instead of solving all the subproblems, to increase the quality of the solution while using orders of magnitude less compute.

Their approach, which they call “learning-to-delegate,” can be used across a variety of solvers and a variety of similar problems, including scheduling and pathfinding for warehouse robots, the researchers say.

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What If Doctors Are Always Watching, but Never There?

🤔

I would prefer it if the data was anonymized and handed back to the patient via an AI interface on the assessment, — Recommended actions and risks involved with each decision. It would then be up to the patient to share the data with a doctor or not, to decide how much data they want to share, and to what extent recommendations can interfere with their day to day life. I’m gonna have a glass of wine. AI: this is your 3rd glass today, do you want to know the risks associated with this decision? No. AI: ok-do you want to monitor vital health statistics in relation to drinking wine instead of water? No. AI; Do you want / Just shut up. Erase all records of my wine drinking and do not monitor this going forward. To live means to die, at least for now. Don’t touch my wine 🍷

Remote technology could save lives by monitoring health from home or outside the hospital. It could also push patients and health care providers further apart.

Revolutionary New AI can be Run Anywhere

The biggest hurdle of developing an Artificial Intelligence which could match the human brain in both efficiency and capabilities is the enormous energy consumption of today’s computer chips. But recent advancements in neural-optimized chips and the emergence of the first neuromorphic computing chips, start painting a clearer picture on how we may soon develop an Artificial Intelligence which matches and even beats us in most areas.

Timing is crucial when it comes to brain computing. It’s the way neurons connect to form circuits. It’s how these circuits analyze extremely complicated data, resulting to life-or-death decisions. It’s the ability of our brains to make split-second judgments, even when confronted with completely novel situations. We accomplish this without frying the brain as a result of excessive energy usage.

To summarize, the brain is a wonderful example of a very powerful computer to imitate, and computer scientists and engineers have already taken the initial steps in this direction.

Did That Chatbot Just Make A Rude Joke?

PolyAI Ltd. is an ambitious startup that creates artificial voices to replace call center operators. Based in London, it has raised $28 million to bring AI-powered customer service to Metro Bank Plc, BP Plc and more. The idea is that instead of the nightmare of dialing random digits in a decision tree, you can instead ask to, say, book a table and a voice — with just the slightest inflection of its machine-learning origins — responds with great civility. That’s nice. But there was a brief moment two years ago when it wasn’t polite at all.

A software developer with PolyAI who was testing the system, asked about booking a table for himself and a Serbian friend. “Yes, we allow children at the restaurant,” the voice bot replied, according to PolyAI founder Nikola Mrksic. Seemingly out of nowhere, the bot was trying make an obnoxious joke about people from Serbia. When it was asked about bringing a Polish friend, it replied, “Yes, but you can’t bring your own booze.”

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Money is pouring into artificial intelligence. Not so much into ethics. That’ll be a problem down the line.

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